A functional CNVR_3425.1 damping lincRNA FENDRR increases lifetime risk of lung cancer and COPD in Chinese

A functional CNVR_3425.1 damping lincRNA FENDRR increases lifetime risk of lung cancer and COPD... Abstract Genomic imbalance referring to somatic variation in chromosome copies represents the most frequent event in tumorigenesis. Germline copy number variations (gCNVs) overlapping regions of genomic imbalance harbor similar structural characteristics and thus influence tumor susceptibility. We aimed to test effects of such gCNVs on the risk of lung cancer and chronic obstructive pulmonary disease (COPD). Genomic imbalance of lung cancer was determined by the array comparative genomic hybridization (aCGH), and common gCNVs at these imbalance regions were genotyped in lung cancer-based and COPD-based retrospective studies. Functional assays were conducted to assess function of promising CNVs. A total of 115 genomic imbalances were discovered occurring at a frequency of more than 25%. The CNVR_3425.1, overlapping the chr16q24.1 with genomic imbalance, was significantly associated with increased risks of lung cancer (OR = 1.76; 95% CI = 1.46–2.11) and COPD (OR = 1.98; 95% CI = 1.57–2.51). The increase copy of CNVR_3425.1 forms a new additional truncated FOXF1 adjacent non-coding developmental regulatory RNA (FENDRR) sequences comparing the gene promoter and perturbs the transcriptional factors (TFs) binding to the original FENDRR promoter and further downregulates FENDRR, a long intergenic non-coding RNA (lincRNA) that functions to inhibit lung cancer by affecting expressions of an abundant number of genes, including the tumor suppressor FOXF1. FENDRR can upregulate FOXF1 by competitively binding to miR-424. The TFs early growth response 1 (EGR1) and transcription factor AP-2 alpha (TFAP2A) were further found to involve the CNVR_3425.1-mediated FENDRR dysregulation. These findings suggested the CNVR_3425.1 to be a possibly predictive biomarker for the risk of lung cancer and COPD, and targeted molecular therapy pertaining to FENDRR upregulation may be a valuable pathway to fight two diseases. Introduction In recent years, several association studies especially genome-wide association studies have identified a lot of single nucleotide polymorphisms (SNPs) to be idiotypic risk indicators of human diseases. There have been lofty expectations from these SNPs to improve precision prediction of disease risk, but till now appear to have had limited effect. Robert K. Nam et al. reported that a nomogram incorporating several genome-wide association study (GWAS) SNPs and other host factors only had a middling value in the diagnosis of prostate cancer (1). Qian D.C. et al. also demonstrated that although SNPs contributed a large increase in risk prediction of lung cancer, the value was not high (2). Such inadequacies were also observed for other diseases (3–5). Several reasons have been proposed for this defect, a major of which is ‘missing heritability’ (6). ‘Missing heritability’ refers to those SNPs that cannot account for all of the heritability of diseases on account of other kinds of genetic variant such as copy number variation (CNV) (7,8). Since a person’s susceptibility to disease depends on a combined effect of all disease-associated variants, we still need to put in greater efforts on discovering missing heritability (8–10). Germline copy number variations (gCNV) is one of the major genetic components of missing heritability, which has been recognized as a contributor of cancer risk (11,12). Characterized as loss or gain of stretches of DNA, gCNVs have a greater influence on affecting function of covered genes than SNPs, which harbor great contributions to susceptibility of human disease. Genomic imbalance refers to a genome showing any somatic loss or gain of DNA sequences compared with the reference DNA whole sequence of the genome of interest (13). It is the most frequent event in tumorigenesis and typically drives cancer with alterations in embedded genes (14–16). Relative to genomic imbalance, gCNVs are loss or gain of DNA sequences that may cause effects as similar as genomic imbalance on embedded genes but can be inheritable. Of late, gCNVs have been suggested to be a crucial factor affecting carcinogen-induced genetic imbalance (17,18). On account of those genomic imbalances pinpointing various oncogenes and tumor suppressors including coding and RNA genes, we hypothesized that gCNVs overlapping regions with genomic imbalance of lung cancer contribute to the risk of lung cancer. In accordance with this, we performed array comparative genomic hybridization (aCGH) to identify genomic imbalance regions of lung cancer and conducted a two-stage retrospective study with a total of 2072 lung cancer patients and 2077 normal controls to test associations between common gCNVs in genomic imbalance regions and lung cancer risk. We also analyzed effects of promising gCNVs on the risk of chronic obstructive pulmonary disease (COPD) with a total of 1025 COPD patients and 1061 normal lung function controls because COPD is a possible intermediate phenotype of lung cancer. We further carried out a series of experiments to test functions of promising gCNVs. Materials and methods Study subjects All study subjects have been described in previously published studies (19). In brief, this study totally recruited 1056 lung cancer cases and 1056 healthy controls as well as 1025 COPD patients and 1061 normal lung function controls from Guangzhou region of China and 1016 lung cancer cases and 1021 normal controls as well as 365 COPD cases and 388 normal lung function controls from Suzhou city in China in total. The southern Chinese population was used as a discovery set, and the eastern population was used as a validation set. None of the COPD cases have lung cancer. Detailed information on recruitment of subjects are described in Supplementary Methods, available at Carcinogenesis Online. Meanwhile, 52 pairs of lung cancer tissues and corresponding non-tumor normal tissues were also collected during surgical excision. Each subject had signed a written informed consent. This study was approved by the institutional review boards of Guangzhou Medical University and Soochow University. In addition, the use of mouse was followed along with the institutional review boards of Guangzhou Medical University. Agilent aCGH analysis Genomic imbalances were tested in eight randomly selected pairs of lung cancer tissues and adjacent normal tissues using the Agilent SurePrint G3 human CGH 4 × 180K array supplied by a commercial company (Biotechnology Corporation, Shanghai, China). CNV selection and detection According to gCNVs information of East Asian population (20), there are a total of 58 common gCNVs (altered copy number frequency, ACNF > 10%) that are located in described genomic imbalance regions of lung cancer. We further selected eight promising gCNVs, whose chromosome regions bear well-established oncogene or tumor suppressor. Copy numbers of these gCNVs were first detected in randomly selected 110 pairs of cases and age- and sex-matched controls with the Accucopy assay (12). Then, the promising CNVR_3425.1 was genotyped in all subjects using the Taqman assay. The results were 95.0% concordant between the two assays. Detailed information on genotyping is presented in Supplementary Methods, available at Carcinogenesis Online. Cell culture The human lung cancer cell lines A549 and PC-9 were purchased from Cell Bank of Type Culture Collection of the Chinese Academy of Science (Shanghai Institute of Cell Biology, Shanghai, China). Both cell lines were tested and authenticated by the standard short tandem repeat DNA typing methodology before used in this study. All cells were cultured in RPMI1640 medium (Gibco, life technologies, California) supplemented with 10% fetal bovine serum (FBS). Cells were placed in a CO2 incubator (SANYO Electric Co., Ltd., Japan) with constant 90% humidity and 5% CO2. Gene expression examination On account of that gCNVs may influence expression of embedded genes (21,22), we asked whether expressions of genes residing in CNVR_3425.1 are affected by it. Messenger RNA (mRNA) levels of four coding genes that are forkhead box F1 (FOXF1), methenyltetrahydrofolate synthetase domain containing (MTHFSD), forkhead box C2 (FOXC2) and forkhead box L1 (FOXL1), and two RNA genes that are FOXF1 adjacent non-coding developmental regulatory RNA (FENDRR), FOXC2 antisense RNA 1 (FOXC2-AS1) were examined using the SYBR-Green quantitative real-time PCR (qRT-PCR). Primers for these genes are shown in Supplementary Table S1, available at Carcinogenesis Online. FENDRR promoter luciferase reporter assay Given that the CNVR_3425.1 covers a majority of intron 1, exon 1, 5′-untranlated region (5′-UTR) and upstream promoter of FENDRR, we asked whether this new additional truncated FENDRR sequences perturbs FENDRR transcriptional activity. We constructed luciferase reporters carrying single copy or dual copies of the FENDRR promoter via cloning a 1400 bp fragment (from −1400 to 0 bp relative to transcriptional initiation site) or a 2900 bp fragment comprising double 1400 bp fragments and a connected short sequence of FENDRR 5′-UTR (from 0 to 100 bp relative to transcriptional initiation site) into the pGL3.1 vector because sequence analysis (http://www.genecards.org/cgi-bin/carddisp.pl?gene=FENDRR) showed that the 1400 bp sequences has the basic characteristic of promoter with two concentrated transcription factors’ (TFs’) binding sites (from −1164 to −906 bp and from −471 to −221 bp relative to transcriptional initiation site) in upstream of FENDRR. The luciferase reporters were defined as pGL3.1-1 copy and pGL3.1–2 copies, respectively. We further mutated the pGL3.1–2 copies reporter at the two TFs’ binding sites of the 5′ terminal FENDRR promoter by deleting these sequences (pGL3.1–2 copies truncated). The protocol for in vitro luciferase assay was in accordance with the standard. A concentration gradient from 5, 10, 50, 100, 500 to 1000 ng of pGL3.1 vectors and a 10 ng of referential plasmid namely pRL-TK were co-transfected into two lung cancer cell lines, A549 and PC-9. RNA interference and ChIP We would like to know more about which TFs involving the dysregulation of FENDRR transcription related to the CNVR_3425.1. To reveal TFs with potentials on targeting the FENDRR promoter, RNA interference and chromatin immunoprecipitation (CHIP) assay were used. In experiments of RNA interference, a total of nine small interfering RNAs (siRNAs) were designed and synthesized to target the coding sequences of four predicted TFs toward binding the FENDRR promoter that are early growth response 1 (EGR1), EGR2, SP1 and transcription factor AP-2 alpha (TFAP2A; https://www-bimas.cit.nih.gov/molbio/proscan/). After transfection of 50 nM siRNA, expression levels of these TFs were tested in A549 cells followed by 12 h using the qRT-PCR. Primers for these TFs are shown in Supplementary Table S1, available at Carcinogenesis Online. The effective siRNAs (75 nM) were then co-transfected with the pGL3.1-1 copy or pGL3.1–2 copies vector (500 ng) to show their effects on transcriptional activity of the FENDRR promoter. A siRNA with random sequences transfection was used as a control (Mock). The sequences for siRNAs are shown in Supplementary Table S2, available at Carcinogenesis Online. A siRNA to target the coding sequence of FOXF1 was also synthesized as suggested (23). The ChIP assay was used for mapping the in vivo distribution of TFs associated with the FENDRR promoter following the standard protocol of One-Day ChIP Kits (EZ-Magna ChIP™ A/G, Darmstadt, Germany). Two pairs of primer were designed for PCR analysis. Their sequences are shown in Supplementary Table S3, available at Carcinogenesis Online. Construction of the lentivirus vector of FENDRR The whole cDNA of FENDRR was synthesized and cloned into the lentiviral expression vector pEZ-Lv201 (Genecopoeia Biotech Co. Ltd., Guangzhou, China). The empty pEZ-Lv201 was used as a control. The pEZ-Lv201-FENDRR was further used to construct a lentiviral vector containing truncated version of FENDRR with miR-424 binding site absence by site-directed mutagenesis using the Quick-Change site-directed mutagenesis kit (Stratagene, La Jolla, California). Detailed protocol is presented in Supplementary Methods, available at Carcinogenesis Online. Cell phenotypic experiments The lentiviral vector was used for construction of FENDRR stably overexpressed cells and referential cells. The cell counting kit-8 assay and flow cytometry analysis were performed to measure cell proliferation, cell cycle and apoptosis. The Transwell assay with uncoated or Matrigel-coated (BD Biosciences, California) Boyden chambers was conducted to assess cell migration and invasion. The tablet cloning experiment and soft-agar colony assay were applied for inspecting in vitro clonogenesis ability of cells. The nude mouse tumorigenicity assay was employed to measure the in vivo tumorigenesis abilities of cells. The protocols for above assays are described in Supplementary Methods, available at Carcinogenesis Online. Gene expression profiling analysis The whole human genome oligo microarray was used to assess alteration of gene expression profiles mediated by FENDRR by a commercial company (Biotechnology Corporation, Shanghai, China). Bioinformatics analysis on public records of FENDRR We quested the Cancer Genome Atlas (TCGA) database to validate expressional status of FENDRR in lung cancer and its correlation with cancer survival as well as to explore its related coding genes by constructing FENDRR co-expression network. Available data on FENDRR expression were derived from 979 non-small cell lung cancer (NSCLC) patients in the TCGA data portal (https://portal.gdc.cancer.gov/). The weighted correlation network analysis in R software was used to achieve expression module, and the software Cytoscape was applied for constructing the network diagram. FOXF1 3′-UTR luciferase reporter assay and microRNA mimics treatment Since FOXF1 was found to be regulated by FENDRR, we asked what the regulatory mechanism is. Moran N Cabili et.al. have revealed that FENDRR is localized in both nucleus and cytosol (24), implying a possible role of FENDRR as post-transcriptional regulator such as competing endogenous RNA. We thus performed bioinformatics analysis with the starbase v2.0 website to forecast possible FENDRR-FOXF1-miRNA interactions (http://starbase.sysu.edu.cn/mirLncRNA.php) and found that FENDRR and FOXF1 share binding sites of seven microRNAs including miR-15a, miR-15b, miR-16, miR-195, miR-424, miR-496 and miR-708-5p. The 3′-UTR of FOXF1 was cloning into the Psi-CHECK2 luciferase vector. The mimics of above microRNAs were synthesized and used at a concentration of 50 nM in followed luciferase assay. A microRNA mimics with random sequences transfection was used as a control (Mock). Moreover, the FOXF1 expression was further tested in A549 cells after 8 h followed by transfection of 100 nM miR-424 mimics (6 h) and treatment of 2 mg/L actinomycin D (2 h, Sigma, MO). The proliferation of cells in response to miR-424 treatment (100 nM) or FOXF1 siRNA (75 nM) treatment was also assessed. Statistical analysis Differences in frequency of copy number between cases and controls were evaluated using the McNemar’s χ2 test. Associations between the CNVR_3425.1 and disease risk were tested by the multivariable logistic regression using the ‘PROC logist’ with adjustment for age, sex, pre-existing TB, pack-year smoked, house ventilation, biomass usage and occupational exposure to metallic toxicant, which were characteristic as risk factors of both diseases in our previous study (25). Interaction between risk factors and the CNVR_3425.1 was assessed using the multiplicative interaction. The Breslow–Day test was applied for analyzing whether the results were homogenous between stratified ORs. The Log-rank test and Cox model with adjustment for age, sex, smoking status histological types and clinical stages were used to evaluate the effect of FENDRR expression on lung cancer survival. Study power was calculated using the PS Software. Differences in numeric data were tested with the Student’s t-test or the one-way ANOVA test. Differences in gene expression between cancer tissues and normal ones were assessed by the paired t-test. Correlation between FENDRR and FOXF1 was tested by the Pearson correlation analysis. All tests were two-sided using the SAS software (version 9.3; SAS Institute, Cary, North Carolina). P < 0.05 was considered to be statistically significant. Results Characterization of genomic imbalances in lung cancer The aCGH analysis detected a total of 325 regions with genomic imbalance, ranging from focal rearrangements (70 kb–5 Mb) to chromosome-arm alterations with chromosomal segments amplification or deletion (Figure 1a). The detailed data has been submitted to the GEO database (GSE89927: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89927). Among these mutations, a total of 115 regions occurred at a frequency of more than 25% (Supplementary Table S4, available at Carcinogenesis Online). Figure 1. View largeDownload slide Genomic imbalance regions of lung cancer including chr16q24.1 and genotyping of the CNVR_3425.1. (a) The Circos for visualization of the aCGH array-discovered 325 genomic imbalance regions of lung cancer in human genome. Blue irregular cords refer to chromosomal segment amplification and jacinth irregular cords refer to chromosomal segment loss. (b) The aCGH array showing copy number gain or loss of chr16 (up) and genomic imbalances occurring at chr16q23.1–24.3 (below). Red dot implies loss and blue dot implies gain at the genomic site. (c) Chromosome location of the CNVR_3425.1 and genes residing in it. (d) The Accucopy assay was performed to determine copy number of the CNVR_3425.1. Bar height corresponds to the mean for two different probes. (e) The Taqman assay was conducted to measure copy number of the CNVR_3425.1. Bar height corresponds to the mean, and error bars represent SD for three technical replicates. Figure 1. View largeDownload slide Genomic imbalance regions of lung cancer including chr16q24.1 and genotyping of the CNVR_3425.1. (a) The Circos for visualization of the aCGH array-discovered 325 genomic imbalance regions of lung cancer in human genome. Blue irregular cords refer to chromosomal segment amplification and jacinth irregular cords refer to chromosomal segment loss. (b) The aCGH array showing copy number gain or loss of chr16 (up) and genomic imbalances occurring at chr16q23.1–24.3 (below). Red dot implies loss and blue dot implies gain at the genomic site. (c) Chromosome location of the CNVR_3425.1 and genes residing in it. (d) The Accucopy assay was performed to determine copy number of the CNVR_3425.1. Bar height corresponds to the mean for two different probes. (e) The Taqman assay was conducted to measure copy number of the CNVR_3425.1. Bar height corresponds to the mean, and error bars represent SD for three technical replicates. gCNVs in genomic imbalances and lung cancer risk Eight common gCNVs located in above 115 regions were selected to genotype with regard to well-established tumor-related genes residing in their regions. They are CNVR_1167.2 at Chr5p15.33, CNVR_1215.1 at Chr13.3, CNVR_1372.1 at Chr5q35.3, CNVR_1994.1 at 8q24.3, CNVR_3339.1 at 16p11.2, CNVR_3367.1 at 16q13, CNVR_3425.1 at 16q24.1 and CNVR_3916.1 at 20q13.13. Figure 1b exhibits one example of region with genomic imbalance at chr16q24.1, and Figure 1c demonstrates chromosome location of the CNVR_3425.1 and its embedded genes. Results from the Accucopy assay showed that the frequency distribution of copy number of CNVR_3425.1 at chr16q24.1 was significantly different between lung cancer cases and matched controls (P = 0.014; Table 1). No significant deviation was observed for other gCNVs (P > 0.05 for all; Table 1). Genotyping of the CNVR_3425.1 by the Accucopy assay is presented in Figure 1d and those of other ones in Supplementary Figure S1, available at Carcinogenesis Online. Table 1. Calculated copy number of candidate gCNVs in lung cancer cases and controls in the discovery phase CNV  Chromosome locationa  Cytobanda  Residing genes  Variation typeb  Reported frequencyb  Case (N = 110)  Control (N = 110)  P-value*  Copy numbercN (%)  Copy numbercN (%)  CNVR_1167.2  Chr5: 801 638–878 490  5p15.33  ZDHHC11  Gain  13.3%  87 (79.1)/23 (20.9)  91 (82.7)/19 (17.3)  0.450  CNVR_1215.1  Chr5: 32 142 258–32 203 868  5p13.3  PDZD2, GOLPH3  Gain  10%  94 (85.5)/16 (14.5)  99 (90.0)/11 (10.0)  0.317  CNVR_1372.1  Chr5:179 927 364–179 927 831  5q35.3  CNOT6  Loss  6.7%  104 (94.5)/6(5.5)  103 (93.6)/7(6.4)  0.763  CNVR_1994.1  Chr8:145 694 587–145 728 609  8q24.3  PPP1R16A, GPT, MFSD3, RECQL4, LRRC14, LRRC24, MGC70857, KIAA1688, CR612338, C8orf82, 09457, AK094577  Gain  6.7%  101 (91.8)/9 (8.2)  106 (96.4)/4 (3.6)  0.132  CNVR_3339.1  Chr16: 28 515 895–28 534 480  16p11.2  SULT1A1  Loss  33.3%  85 (77.3)/25 (22.7)  74 (67.3)/36 (32.7)  0.086  CNVR_3367.1  Chr16:55 256 326–55 267 342  16q13  MT1G, MT1H, MT1IP  Gain/loss  13.3%  7 (6.4)/91 (82.7)/12 (10.9)  5 (4.5)/96 (87.3)/9 (8.2)  0.317  CNVR_3425.1  Chr16:85 074 275–85 178 636  16q24.1  FOXF1, MTHFSD, FOXC2, FOXL1, FENDRR, FOXC2-AS1  Gain  6.7%  88 (80.0)/22 (22.0)  100 (90.9)/10 (9.1)  0.014  CNVR_3916.1  Chr20:48 238 984–48 243 094  20q13.13  CEBPB, CEBPB-AS1  Gain  10%  90 (81.8)/20 (18.2)  98 (89.1)/12 (10.9)  0.114  CNV  Chromosome locationa  Cytobanda  Residing genes  Variation typeb  Reported frequencyb  Case (N = 110)  Control (N = 110)  P-value*  Copy numbercN (%)  Copy numbercN (%)  CNVR_1167.2  Chr5: 801 638–878 490  5p15.33  ZDHHC11  Gain  13.3%  87 (79.1)/23 (20.9)  91 (82.7)/19 (17.3)  0.450  CNVR_1215.1  Chr5: 32 142 258–32 203 868  5p13.3  PDZD2, GOLPH3  Gain  10%  94 (85.5)/16 (14.5)  99 (90.0)/11 (10.0)  0.317  CNVR_1372.1  Chr5:179 927 364–179 927 831  5q35.3  CNOT6  Loss  6.7%  104 (94.5)/6(5.5)  103 (93.6)/7(6.4)  0.763  CNVR_1994.1  Chr8:145 694 587–145 728 609  8q24.3  PPP1R16A, GPT, MFSD3, RECQL4, LRRC14, LRRC24, MGC70857, KIAA1688, CR612338, C8orf82, 09457, AK094577  Gain  6.7%  101 (91.8)/9 (8.2)  106 (96.4)/4 (3.6)  0.132  CNVR_3339.1  Chr16: 28 515 895–28 534 480  16p11.2  SULT1A1  Loss  33.3%  85 (77.3)/25 (22.7)  74 (67.3)/36 (32.7)  0.086  CNVR_3367.1  Chr16:55 256 326–55 267 342  16q13  MT1G, MT1H, MT1IP  Gain/loss  13.3%  7 (6.4)/91 (82.7)/12 (10.9)  5 (4.5)/96 (87.3)/9 (8.2)  0.317  CNVR_3425.1  Chr16:85 074 275–85 178 636  16q24.1  FOXF1, MTHFSD, FOXC2, FOXL1, FENDRR, FOXC2-AS1  Gain  6.7%  88 (80.0)/22 (22.0)  100 (90.9)/10 (9.1)  0.014  CNVR_3916.1  Chr20:48 238 984–48 243 094  20q13.13  CEBPB, CEBPB-AS1  Gain  10%  90 (81.8)/20 (18.2)  98 (89.1)/12 (10.9)  0.114  aReferenced by the UCSC with NCBI36/hg18 database (http://genome.ucsc.edu/). bInformation reported by Park H et al. (20). cCopy number is exhibited as 2-copy/≥3-copy for gain-type CNV, 2-copy/≤1-copy for loss-type CNV, and ≤1-copy/2-copy/≥3-copy for gain/loss-type CNV. *P-value calculated by the McNemar’s χ2 test for matched case-control data. View Large CNVR_3425.1 increases the risk of lung cancer and COPD Distributions of demographic variables, selected risk factors and clinical features of all studied samples are shown in Supplementary Table S5, available at Carcinogenesis Online and have been described elsewhere (19,26). We only genotyped the CNVR_3425.1 with promising significance and detected more than three genotypes of copy number using the Taqman assay (Figure 1e), including common 2-copy genotype and more than 2-copy genotype that were described as ≥3 copy. As shown in Table 2, compared with the 2-copy carriers, these ≥3-copy ones harbored a significantly increase in risk for developing lung cancer by 70% (OR = 1.70; 95% CI = 1.33–2.18; P = 2.75 × 10–5) in southern Chinese. Data from the eastern Chinese population further confirmed this observation. The ≥3-copy contributed a significantly higher risk to lung cancer than the 2-copy (OR = 1.85; 95% CI = 1.40–2.43; P = 2.75 × 10–5). When merged the two populations, the ≥3-copy predisposed carriers for developing lung cancer when compared with the 2-copy (OR = 1.76; 95% CI = 1.46–2.11; P = 1.86 × 10–9). Moreover, the ≥3-copy consistently increased the risk of COPD by 116, 74 and 98% when compared with the 2-copy in the southern Chinese (OR = 2.09; 95% CI = 1.59–2.76; P = 1.50 × 10–7), eastern Chinese (OR = 1.74; 95% CI = 1.11–2.72; P = 0.015) and merged populations (OR = 1.98; 95%CI = 1.57–2.51; P = 1.28 × 10–8), respectively. Table 2. Association between the CNVR_3425.1 and the risk of lung cancer and COPD in Chinese Copy number  Cases N (%)a  Controls N (%)a  Crude OR (95% CI)  Adjusted OR (95% CI)b  Lung cancer  Southern Chinese (discovery set)  1056  1056      2-copy  849 (81.0)  919 (87.9)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  199 (19.0)  127 (12.1)  1.70 (1.33–2.16)  1.70 (1.33–2.18)  Eastern Chinese (validation set)  1016  1021      2-copy  842 (83.8)  918 (90.6)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  163 (16.2)  95 (9.4)  1.87 (1.43–2.45)  1.85 (1.40–2.43)  Merged population  2072  2077      2-copy  1691 (82.4)  1837 (89.2)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  362 (17.6)  222 (10.8)  1.77 (1.48–2.12)  1.76 (1.46–2.11)  COPD          Southern Chinese (discovery set)          2-copy  850 (83.3)  958 (91.0)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  170 (16.7)  95 (9.0)  2.02 (1.54–2.64)  2.09 (1.59–2.76)  Eastern Chinese (validation set)  365  388      2-copy  290 (83.8)  326 (89.8)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  56 (16.2)  37 (10.2)  1.70 (1.09–2.65)  1.74 (1.11–2.72)  Merged population          2-copy  1140 (83.5)  1284 (90.7)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  226 (16.5)  132 (9.3)  1.93 (1.53–2.43)  1.98 (1.57–2.51)  Copy number  Cases N (%)a  Controls N (%)a  Crude OR (95% CI)  Adjusted OR (95% CI)b  Lung cancer  Southern Chinese (discovery set)  1056  1056      2-copy  849 (81.0)  919 (87.9)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  199 (19.0)  127 (12.1)  1.70 (1.33–2.16)  1.70 (1.33–2.18)  Eastern Chinese (validation set)  1016  1021      2-copy  842 (83.8)  918 (90.6)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  163 (16.2)  95 (9.4)  1.87 (1.43–2.45)  1.85 (1.40–2.43)  Merged population  2072  2077      2-copy  1691 (82.4)  1837 (89.2)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  362 (17.6)  222 (10.8)  1.77 (1.48–2.12)  1.76 (1.46–2.11)  COPD          Southern Chinese (discovery set)          2-copy  850 (83.3)  958 (91.0)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  170 (16.7)  95 (9.0)  2.02 (1.54–2.64)  2.09 (1.59–2.76)  Eastern Chinese (validation set)  365  388      2-copy  290 (83.8)  326 (89.8)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  56 (16.2)  37 (10.2)  1.70 (1.09–2.65)  1.74 (1.11–2.72)  Merged population          2-copy  1140 (83.5)  1284 (90.7)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  226 (16.5)  132 (9.3)  1.93 (1.53–2.43)  1.98 (1.57–2.51)  aAbout 1.48% of all samples were failed to determine copy number due to the DNA quality or unknown reason. bAdjusted in a logistic regression model that included age, sex, pre-existing TB, pack-year smoked, house ventilation, biomass usage and occupational exposure to metallic toxicant. View Large In addition, stratification analysis showed no significant difference in the association between CNVR_3425.1 and the risk of lung cancer as well as COPD (Supplementary Table S6 and S7, available at Carcinogenesis Online). Also, no significant interaction was observed between any surrounding factors and the gCNV (Supplementary Table S6 and S7, available at Carcinogenesis Online). CNVR_3425.1 significantly affects expression of FENDRR but not other embedded genes The gene expression test showed that the lung cancer tissues carrying the ≥3-copy of CNVR_3425.1 (n = 22) exerted a significantly lower expression of FENDRR than those carrying the 2-copy (n = 30; mean ± standard deviation: 0.028 ± 0.043 versus 0.052 ± 0.037; P = 0.037; Figure 2a). The ≥3-copy was also correlated with a decreased expression of FOXF1 in comparison with the 2-copy (0.605 ± 0.591 versus 0.968 ± 0.943). However, the difference was not significant (P = 0.119; Figure 2a). Furthermore, such an effect was not observed for other genes (P > 0.05 for all; Supplementary Figure S2, available at Carcinogenesis Online). Figure 2. View largeDownload slide Biological effect of the CNVR_3425.1 on FENDRR expression as well as FOXF1 expression. (a) The qRT-PCR was performed to assess the expression of FENDRR (left) as well as FOXF1 (right) in lung tissues carrying different copy of the CNVR_3425.1. P-values are calculated by the Student’s t-test. (b) Schematic of the reporter genes containing the 1-copy or 2-copy or 2-copy truncated version of FENDRR promoter included in the CNVR_3425.1. TIS: transcriptional initiation site. TFs’ binding site 1: from −1164 to −906 bp relative to TIS; TFs’ binding site 2: from −471 to −221 bp relative to TIS. (c) The luciferase assay was used to test differences in transcriptional activity between pGL3.1 reporters carrying the different copy versions of FENDRR promoter in conventional A549 cells (left) and PC-9 cells (right). The firefly luciferase was used to show transcriptional activity of the FENDRR promoter, and the renilla luciferase was used as the internal standard (i.e., Fluc/Rluc). *P < 0.05, calculated by the one-way ANOVA test. (d) The luciferase assay was used to measure the transcriptional activity of FENDRR promoter (pGL3.1-1 copy) in response to TFs’ siRNAs treatment in conventional A549 cells. *P < 0.05, calculated by the Student’s t-test. (e) The luciferase assay was conducted to measure the transcriptional activity between pGL3.1 reporters carrying the different copy versions of FENDRR promoter in response to treatments of EGR1 siRNAs or TFAP2A siRNAs in conventional A549 cells. f. The ChIP was performed to analyze EGR1 (left) and TFAP2A (right) recruitment at selected loci (−1163 to −016 bp relative to TIS) of the FENDRR promoter in conventional A549 cells. Input was used as a genomic DNA-based positive control, GAPDH was used as a ChIp-based positive control, IgG was used as a negative control and H2O was used as a blank control. Bar height corresponds to the mean, and error bars represent SD for three technical replicates. Figure 2. View largeDownload slide Biological effect of the CNVR_3425.1 on FENDRR expression as well as FOXF1 expression. (a) The qRT-PCR was performed to assess the expression of FENDRR (left) as well as FOXF1 (right) in lung tissues carrying different copy of the CNVR_3425.1. P-values are calculated by the Student’s t-test. (b) Schematic of the reporter genes containing the 1-copy or 2-copy or 2-copy truncated version of FENDRR promoter included in the CNVR_3425.1. TIS: transcriptional initiation site. TFs’ binding site 1: from −1164 to −906 bp relative to TIS; TFs’ binding site 2: from −471 to −221 bp relative to TIS. (c) The luciferase assay was used to test differences in transcriptional activity between pGL3.1 reporters carrying the different copy versions of FENDRR promoter in conventional A549 cells (left) and PC-9 cells (right). The firefly luciferase was used to show transcriptional activity of the FENDRR promoter, and the renilla luciferase was used as the internal standard (i.e., Fluc/Rluc). *P < 0.05, calculated by the one-way ANOVA test. (d) The luciferase assay was used to measure the transcriptional activity of FENDRR promoter (pGL3.1-1 copy) in response to TFs’ siRNAs treatment in conventional A549 cells. *P < 0.05, calculated by the Student’s t-test. (e) The luciferase assay was conducted to measure the transcriptional activity between pGL3.1 reporters carrying the different copy versions of FENDRR promoter in response to treatments of EGR1 siRNAs or TFAP2A siRNAs in conventional A549 cells. f. The ChIP was performed to analyze EGR1 (left) and TFAP2A (right) recruitment at selected loci (−1163 to −016 bp relative to TIS) of the FENDRR promoter in conventional A549 cells. Input was used as a genomic DNA-based positive control, GAPDH was used as a ChIp-based positive control, IgG was used as a negative control and H2O was used as a blank control. Bar height corresponds to the mean, and error bars represent SD for three technical replicates. CNVR_3425.1 influences the transcriptional efficiency of FENDRR promoter The construction features of three reporters comprising the different copies of FENDRR promoter are shown in Figure 2b. Only at high concentrations of reporter genes (≥50 ng), the duplicated copies of FENDRR promoter showed significantly weaker luciferase activity when compared with the single copy of FENDRR promoter in both A549 and PC-9 cells, while as expected, the duplicated copies of truncated FENDRR promoter, missing two predicted TFs’ binding sites, exerted approaching luciferase activity in comparison with the single copy (Figure 2c). At low concentrations, no significant difference was observed between the three types of reporter genes. Knockdown of EGR1 and TFAP2A inhibits activity of the FENDRR promoter Since almost all siRNAs showed reduced effects on target genes, we selected two siRNAs for each gene according to the degradation effectivity (Supplementary Figure S3a–d, available at Carcinogenesis Online). The luciferase assay further revealed that knockdown of EGR1 or TFAP2A by any one siRNA showed consistently decreased transcriptional activity in comparison with mock transfection (P < 0.05 for all; Figure 2d). Moreover, following co-transfection of the EGR1/TFAP2A siRNAs and the pGL3.1-1 copy or pGL3.1–2 copies’ reporters into A549 cells, these siRNAs diminished the difference of luciferase activity between the two reporters (Figure 2e). The ChIP assay further confirmed that both EGR1 and TFAP2A bound to the FENDRR promoter (Figure 2f, Supplementary Figure S3e, available at Carcinogenesis Online), but EGR2 and SP1 did not (Supplementary Figure S3f and g, available at Carcinogenesis Online). FENDRR expression was correlated with lung cancer survival With the TCGA data, we stratified the FENDRR expression by median level of lung cancer tissues. Expressed level equal or greater than median was defined as high, while less than median was defined as low. High expressed FENDRR conferred longer survival time (1790 days versus 1346 days, Log-rank test P = 0.088) and lower fatality rate (hazard ratio = 0.50, 95% CI = 0.25–0.98) when compared with low expressed FENDRR. FENDRR functions to inhibit tumor growth FENDRR was downregulated in 76.9% (40/52) cancer tissues when compared with normal tissues (P = 0.025; Figure 3a). The TCGA data further confirmed that FENDRR expressed a significantly lower level in lung cancer tissues than their normal counterparts in both lung adenocarcinoma (log2(FoldChange) = −3.981, P= 1.98 × 10−80) and lung squamous cell carcinoma (log2(FoldChange) = −4.079, P= 2.92 × 10−71). Additionally, FENDRR was almost undetectable in all lung cancer cell lines. Having established overexpression of FENDRR in transfected A549 and PC-9 cells, we examined effects of FENDRR on phenotypes of lung cancer cells. We found that FENDRR overexpression well reduced the rate of cell proliferation at levels beginning with 1000 cells per well and 500 cells per well in both cells (Figure 3b). Overexpression of FENDRR led more A549 cells to rest on G0–G1 phase and less PC-9 cells to keep in G2–M phase (P < 0.05 for both; Figure 3c). However, overexpression of FENDRR exerted no significant effect on cell apoptosis (P > 0.05 for both; Figure 3d). Furthermore, FENDRR overexpression resulted in sharply declined tumor growth in vitro (P < 0.05 for all; Figure 3e and f). The A549 and PC-9 cells with high expression of FENDRR exhibited significantly decreased growth rate of tumor xenograft compared with their controls in vitro (Figure 4a–c). Figure 3. View largeDownload slide FENDRR functions to inhibit lung tumor growth in vitro. Both A549 and PC-9 cells were transfected with pEZ-Lv201-FENDRR and pEZ-Lv201-Empty. (a) The qRT-PCR was performed to assess the expression of FENDRR in 52 pairs of lung cancer tissues and corresponding non-tumor normal tissues. Bar height corresponds to the log mean difference between tumor tissues and normal tissues from three technical replicates. P-value is calculated by the paired t-test. (b) The cell counting kit-8 assay was conducted to determine the proliferation of A549 and PC-9 cells with concentration of 500 cells per well and 1000 cells per well. Circle dot on line corresponds to the mean; error bars represent SD for seven biological replicates. (c) The flow cytometry was conducted to determine the cell cycle of A549 and PC-9 cells. (d) The flow cytometry was conducted to determine the cell apoptosis of A549 and PC-9 cells. Bar height corresponds to the mean, and error bars represent SD for three biological replicates. (e) and (f) The plate colony (e) and soft-agar experiments (f) were conducted to test for tumorigenicity of A549 and PC-9 cells. Bar height corresponds to the mean, and error bars represent SD for six biological replicates. *P < 0.05, **P < 0.01, ***P < 0.001, calculated by the Student’s t-test. Figure 3. View largeDownload slide FENDRR functions to inhibit lung tumor growth in vitro. Both A549 and PC-9 cells were transfected with pEZ-Lv201-FENDRR and pEZ-Lv201-Empty. (a) The qRT-PCR was performed to assess the expression of FENDRR in 52 pairs of lung cancer tissues and corresponding non-tumor normal tissues. Bar height corresponds to the log mean difference between tumor tissues and normal tissues from three technical replicates. P-value is calculated by the paired t-test. (b) The cell counting kit-8 assay was conducted to determine the proliferation of A549 and PC-9 cells with concentration of 500 cells per well and 1000 cells per well. Circle dot on line corresponds to the mean; error bars represent SD for seven biological replicates. (c) The flow cytometry was conducted to determine the cell cycle of A549 and PC-9 cells. (d) The flow cytometry was conducted to determine the cell apoptosis of A549 and PC-9 cells. Bar height corresponds to the mean, and error bars represent SD for three biological replicates. (e) and (f) The plate colony (e) and soft-agar experiments (f) were conducted to test for tumorigenicity of A549 and PC-9 cells. Bar height corresponds to the mean, and error bars represent SD for six biological replicates. *P < 0.05, **P < 0.01, ***P < 0.001, calculated by the Student’s t-test. Figure 4. View largeDownload slide FENDRR functions to inhibit lung tumor growth in vivo. (a) The BALB/c nude mice were used to determine the growth of tumor, originating injection of A549 and PC-9 cells. Xenografted tumors originating from subcutaneously implanted A549 (up) and PC-9 (below) cells were established. (b) (A549 cells) and (c) (PC-9 cells) Tumor volume was measured every 3 days, until the end of 3rd week (up). Extracted tumor tissues from the mouse were imaged (left bottom) and weighted (right bottom). Circle dot on line or bar height corresponds to the mean; error bars represent SD for six biological replicates. *P < 0.05, **P < 0.01, calculated by the Student’s t-test. Figure 4. View largeDownload slide FENDRR functions to inhibit lung tumor growth in vivo. (a) The BALB/c nude mice were used to determine the growth of tumor, originating injection of A549 and PC-9 cells. Xenografted tumors originating from subcutaneously implanted A549 (up) and PC-9 (below) cells were established. (b) (A549 cells) and (c) (PC-9 cells) Tumor volume was measured every 3 days, until the end of 3rd week (up). Extracted tumor tissues from the mouse were imaged (left bottom) and weighted (right bottom). Circle dot on line or bar height corresponds to the mean; error bars represent SD for six biological replicates. *P < 0.05, **P < 0.01, calculated by the Student’s t-test. Xu TP et al. have reported that FENDER overexpression suppressed invasion and migration of gastric cancer cells, which are features of tumor progression (27). However, as shown in Supplementary Figure S4, available at Carcinogenesis Online, FENDER overexpression did not significantly inhibit both migration and invasion of A549 and PC-9 cells (P > 0.05 for all). FENDRR drives alteration of cell expression profile The gene expression profiling analysis identified a total of 559 differentially expressed transcripts (|log2(FoldChange)| > 2) in response to FENDRR overexpression, including 359 downregulated transcripts and 200 upregulated ones (Figure 5a). The chip data have been submitted to the GEO database (GSE89828: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89828). Figure 5. View largeDownload slide FENDRR upregulates FOXF1 and affects a series of gene expressions. (a) The whole human genome oligo microarray was conducted to determine alteration of gene expression profiles in response to FENDRR overexpression in FENDRR overexpressed A549 cells and referential A549 cells. Red dot marks upregulated genes, and green dot marks downregulated genes. (b) Construction of co-expression network according to expressional correlations between FENDRR and related genes based on TCGA data. (c) The qRT-PCR was conducted to determine the FOXF1 mRNA expression. *P < 0.05, **P < 0.01, calculated by the Student’s t-test. (d) The correlation between the FENDRR expression and the FOXF1 expression in lung tissues. Purple dot corresponds to log(FENDRR) expression in x-axis and log(FOXF1) expression in y-axis. The values of r and P are calculated by the Pearson correlation analysis. (e) The qRT-PCR was performed to assess the expression of FOXF1 in 52 pairs of lung cancer tissues and corresponding non-tumor normal tissues. Bar height corresponds to the log mean difference between tumor tissues and normal tissues from three technical replicates. P-value is calculated by the paired t-test. (f) The luciferase assay was used to test transcriptional activity of FOXF1 3′-UTR in conventional A549 cells and PC-9 cells in response to microRNA mimics treatment. The renilla luciferase was used to show transcriptional activity of the FENDRR promoter, and the firefly luciferase was used as the internal standard (i.e., Rluc/Fluc). (g) The luciferase assay was used to test transcriptional activity of FOXF1 3′-UTR in A549 cells overexpressed FENDRR, A549 cells overexpressed truncated FENDRR and A549 referential cells in response to miR-424 mimics treatment. (h) Expression of FOXF1 in A549 cells overexpressed FENDRR, A549 cells overexpressed truncated FENDRR and A549 referential cells in response to treatment of miR-424 mimics. Bar height corresponds to the mean; error bars represent SD for three biological or technical replicates. P-values are calculated by the Student’s t-test. Figure 5. View largeDownload slide FENDRR upregulates FOXF1 and affects a series of gene expressions. (a) The whole human genome oligo microarray was conducted to determine alteration of gene expression profiles in response to FENDRR overexpression in FENDRR overexpressed A549 cells and referential A549 cells. Red dot marks upregulated genes, and green dot marks downregulated genes. (b) Construction of co-expression network according to expressional correlations between FENDRR and related genes based on TCGA data. (c) The qRT-PCR was conducted to determine the FOXF1 mRNA expression. *P < 0.05, **P < 0.01, calculated by the Student’s t-test. (d) The correlation between the FENDRR expression and the FOXF1 expression in lung tissues. Purple dot corresponds to log(FENDRR) expression in x-axis and log(FOXF1) expression in y-axis. The values of r and P are calculated by the Pearson correlation analysis. (e) The qRT-PCR was performed to assess the expression of FOXF1 in 52 pairs of lung cancer tissues and corresponding non-tumor normal tissues. Bar height corresponds to the log mean difference between tumor tissues and normal tissues from three technical replicates. P-value is calculated by the paired t-test. (f) The luciferase assay was used to test transcriptional activity of FOXF1 3′-UTR in conventional A549 cells and PC-9 cells in response to microRNA mimics treatment. The renilla luciferase was used to show transcriptional activity of the FENDRR promoter, and the firefly luciferase was used as the internal standard (i.e., Rluc/Fluc). (g) The luciferase assay was used to test transcriptional activity of FOXF1 3′-UTR in A549 cells overexpressed FENDRR, A549 cells overexpressed truncated FENDRR and A549 referential cells in response to miR-424 mimics treatment. (h) Expression of FOXF1 in A549 cells overexpressed FENDRR, A549 cells overexpressed truncated FENDRR and A549 referential cells in response to treatment of miR-424 mimics. Bar height corresponds to the mean; error bars represent SD for three biological or technical replicates. P-values are calculated by the Student’s t-test. Gene co-expression network The weighted correlation network analysis result indicated that FENDRR expression was significantly correlated with expressions of 407 coding genes in lung adenocarcinoma and 40 ones in squamous cell carcinoma, of which 31 genes were common (Figure 5b). The 31 genes were also mostly revealed by the microarray, including FENDRR neighboring gene FOXF1. FENDRR upregulates FOXF1 expression via competitively binding to miR-424 The detection of expression showed that FENDRR overexpression upregulated FOXF1 expression (Figure 5c), and their expressions were positively correlated in lung tissues (r = 0.877, P = 5.56 × 10–34; Figure 5d). FOXF1 was also downregulated in lung cancer tissues when compared with adjacent lung normal tissues with a clear tendency to significance (P = 0.099; Figure 5e). The luciferase assay further showed that only miR-424 exerted a significant decrease in luciferase activity of FOXF1 3′-UTR in both A549 and PC-9 cells. Such an effect was not observed for other microRNAs (Figure 5f). The miR-424 mimics caused a significantly less decrease in luciferase activity in A549 cells overexpressed FENDRR (16.9%) than that overexpressed truncated FENDRR (27.7%) and referential cells (33.1%; P < 0.001; Figure 5g). Moreover, after treatment of the miR-424 mimics, the FOXF1 expression in A549 cells overexpressed FENDRR had a less decrease (21.8%) as compared with referential cells (37.7%) with a considerable trend toward significance (P = 0.058), while that in A549 cells overexpressed truncated FENDRR exerted a similar decrease (35.4%) as compared with referential cells (P = 0.810; Figure 5h). In addition, following transfection of miR-424 mimics or FOXF1 siRNA, the FOXF1 siRNA induced a significant increase in proliferation rate, whereas miR-424 mimics did not in comparison with control cells as the cell counting kit assay and the tablet cloning experiment shown (Supplementary Figure S5, available at Carcinogenesis Online). Discussion Our knowledge about roles of gCNVs on disease susceptibility and their affinity mechanisms remains limited. Few studies have reported a limited number of gCNVs to be susceptible loci for different cancers, most of which were based on candidate gene strategy and lack of functional evidences. In the current study, we detected the gCNVs in genomic imbalances of lung cancer and identified the CNVR_3425.1 to be a risk indicator of lung cancer and COPD for Chinese populations. We also explicated the mechanism how the gCNV confer susceptibility of both diseases. To the best of our knowledge, this is the first study to investigate on gCNVs in genomic imbalances and a revelation of long intergenic non-coding RNA (lincRNA)-related gCNV. In this post-GWAS era, gCNV is increasingly drawing attention with respect to their contributions on missing heritability of disease. For lung cancer, several gCNVs that are associated with lung cancer risk have been demonstrated, which highlight the gCNVs as important components of cancer heritability (12,28,29). However, due to the limitations of the candidate gene strategy that focused on gCNVs overlapping well-known cancer-related coding genes, relationships between most of gCNVs and human disease are unknown and are yet to be elucidated. Under the prevailing condition that cannot implement large-scale gCNV detection, our study chose a low thoroughput technology to test eight promising gCNVs in chromosome regions with genomic imbalance of lung cancer. We identified one gCNV namely CNVR_3425.1 in the region chr16q23.1–24.3 with contribution on increasing the risk of lung cancer and COPD in Chinese populations. Coincidentally, a report based on the SNP genotyping chip reported that the risk gCNVs of lung cancer are located on genomic recombination hotspots (28). Also, the genetic mutations of chr16q23.1–24.3 have been reported to be implicated in lung developmental disorder and lung cancer (25,30). These results highlight the importance of gCNVs in genomic imbalances, and broader studies testing more such gCNVs are warranted. Several gCNVs have been supposed to affect disease predisposition via their modulatory effects on embedded genes. Here, we proved that the incremental copy of CNVR_3425.1 exerted a decreased expression of FENDRR. Functional mechanisms of gCNV have been documented as gene dosage effect (21,31), distal enhancer regulation (32,33) and structural mutation induction (34). Here, due to the negative correlation and superimposed pattern between the gCNV and FENDRR, mapping the increased copy of CNVR_3425.1 and decreased FENDRR is an involved problem. Using the luciferase assay, we have identified that the increased copy of CNVR_3425.1 weakens transcriptional efficiency of FENDRR because the 2-copy luciferase reporter showed reduced transcriptional activity in comparison with the 1-copy one, and the 2-copy truncated one indeed rescued the activity, which exerted under specific condition with high concentrations of reporter gene. After determining the responsible TFs EGR1 and TFAP2A, we further found that knockdown of EGR1 and TFAP2A diminished the difference of luciferase activity between the 1-copy reporter and the 2-copy one. The findings prompt us to speculate that the incremental copy-formed promoter can disturb the TF’s binding to the original FENDRR promoter, which depends on low responsible TFs’ expression level or great transcriptional demand. When such TFs reduce expression, which might be, for example, induced by carcinogens stimulus, the detrimental function of CNVR_3425.1 is starting to appear. Consistently, downregulation of TFAP2A was observed to promote lung carcinogenesis in response to cigarette smoke condensate (35). Absent EGR1 was also observed in lung cancer tissues (36). Furthermore, one previous study has documented a long-range regulation of FENDRR promoter depending on physical interaction as chromatin looping between the region that FENDRR and CNVR_3425.1 overlapped and a putative distant regulatory region (DRR) (37). The DRR is located in downstream of FENDRR, which exerted an in trans regulation on the FENDRR promoter. These distal transacting factors might have interaction with local factors such as EGR1. Considering the large scale, CNVR_3425.1 would most probably perturb the physically interaction between the DRR and FENDRR promoter, then disrurbing the TF’s binding to the FENDRR promoter and ultimately leading to decreased FENDRR. However, to further determine this perturbation of large gCNV-mediated chromatin, interactions seem to be impossible in contemporary condition and need to be solved in the future. Further analyses supported a tumor suppressor role of FENDRR in lung cancer. FENDRR is a well-established lincRNA (38), whose absence has been found in lung and gastric tumors, resulting defects in lung and gastrointestinal tract (27,39,40). Yet, its concrete role on lung tumorigenesis remains an enigma. Here, both in vivo and in vitro experiments showed that FENDRR was downregulated in lung tumor tissues, and high expression of FENDRR was correlated with favorite lung cancer survival. Overexpression of it markedly inhibited cell proliferation and tumor growth. All these demonstrably indicated that FENDRR functions to inhibit lung cancer development. Moreover, we have validated FOXF1 to be a target of FENDRR in lung cancer, which is firstly reported to be a target of FENDRR in pluripotent cells (41). FOXF1 is located in close proximity to FENDRR and has been identified to be a tumor suppressor (42). Both our expressional results and TCGA data revealed a significantly positive correlation between FENDRR and FOXF1. Also, knockdown of FOXF1 returned FENDRR-mediated phenotype change. Even it has been known that FENDRR can interact with polycomb repressive complex 2 to facilitate promoter methylation and cause decreased target genes (43), which cannot explain why FENDRR upregulates FOXF1. One computational study has documented possible mechanism on FENDRR regulation of FOXF1 based on bioinformatics analysis (44); yet, the projection lacks experimental evidences. Using the luciferase assay, we have revealed that when presented as a competing endogenous RNA, FENDRR can sponge miR-424, reduce its binding to FOXF1 and then upregulate FOXF1. Consistently, downregulation of miR-424 has been report to act as a cancer suppressor involving lung cancer (45). However, may be due to highly endogenous expression in A549 cells (46), the miR-424 mimics did not rescue the FENDRR-mediated decline of cell proliferation. Noticeably, the increased copy of CNVR_3425.1 tends to exert a decreased expression of FOXF1. This is contradictory to the approbable opinion of gene dosage effect, which should have caused upregulated FOXF1, considering copy increase of CNVR_3425.1 duplicates the FOXF1 gene. Therefore, it seems tempting to speculate that the CNVR_3425.1 first damps FENDRR expression and then indirectly results in a net decrease of FOXF1. In addition, there remains no doubt that FENDRR would cause altered expressions of a series of genes with respect to its epigenetic regulation ability (43) and its effect on FOXF1, which is a probable transcription activator for a number of lung-specific genes (47). Indeed, our data showed that overexpression of FENDRR results in altered expression of abundant genes. Also, TCGA data further revealed an abundant of coding genes to be associated with FENDRR. These findings further supported FENDRR to be involved in lung cancer and COPD development. Thus, upregulation of FENDRR may be exploited in the treatment of the two diseases. It is unlikely that the current finding CNVR_3425.1 confers to increased disease risk was achieved by chance, considering that the association was consistent in two studied populations and two contextual diseases and had strong functional experimental evidences. We have also achieved >99.0% study power for both lung cancer and COPD case-control studies. However, being hospital-based case-control studies, bias such as information or selection bias was inevitable. This might cause an error in estimation on association strength between the gCNV and the risk of lung cancer as well as COPD. Moreover, the functional assays only presented an indirect connection between the CNVR_3425.1 and lung cancer phenotypes pass by FENDRR. We have tried to construct a stable A549 cell line with defined number of integrates of the promoter region that are expected to carry 4-copy or 3-copy using the Crispr/cas9 technology, and thus, we can directly observe the genetic effect of the CNVR_3425.1 on FENDRR expression and cell phenotypes. Unfortunately, we always failed to conduct such an experiment due to technical restriction. Based on case-control studies and a series of functional assays, we have identified the CNVR_3425.1 to be susceptible loci for lung cancer and COPD in Chinese. The ≥3-copy predispose carriers to develop lung cancer and COPD by weakening transcriptional efficiency of the FENDRR promoter and further downregulates FENDRR expression. FENDRR functions to inhibit lung cancer development by affecting expression of an abundant number of genes, including an upregulation of tumor suppressor FOXF1. These data supported the CNVR_3425.1 to be an idiotypic predictor for the risk of lung cancer and COPD in Chinese, and targeted molecular therapy owing to FENDRR upregulation may be valuable pathway to fight two diseases. Supplementary material Supplementary data are available at Carcinogenesis online. Funding This study was supported by the National Natural Scientific Foundation of China grants 81473040, 81673267 (J.Lu) and by 81402753, 81672303 (L.Y). Guangdong Provincial Major Projects Grant 2014KZDXM046 (J.Lu), Guangzhou Science and Technology Program Pearl River Nova projects Grant 201710010049 (L.Y.), Guangzhou Education Bureau Major projects Grant 1201610122 (L.Y.) and Guangdong education Department Characteristic innovation project Grants 1201541589 and 2015KTSCX116 (L.Y.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank Dr. Soham Datta for his assistance on English revision. We thank Dr. Yuan Guo, Dr. Lin Liu, Dr. Dongsheng Huang and Dr. Yumin Zhou for their assistance on subjects’ recruitment. Conflict of Interest Statement The authors declare that they have no competing interests. Authors’ contributions J.L. and L.Y. conceived and designed the experiments; L.Y. analyzed the data and wrote the paper; D.W. and J.C. performed major functional experiments; J.C., Y.L. and L.L. performed genotyping assays; Y.C. and F.Q. performed subsidiary functional experiments; B.Y. contributed reagents/materials/analysis tools; Y.Z. help revise the paper. 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A functional CNVR_3425.1 damping lincRNA FENDRR increases lifetime risk of lung cancer and COPD in Chinese

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Abstract

Abstract Genomic imbalance referring to somatic variation in chromosome copies represents the most frequent event in tumorigenesis. Germline copy number variations (gCNVs) overlapping regions of genomic imbalance harbor similar structural characteristics and thus influence tumor susceptibility. We aimed to test effects of such gCNVs on the risk of lung cancer and chronic obstructive pulmonary disease (COPD). Genomic imbalance of lung cancer was determined by the array comparative genomic hybridization (aCGH), and common gCNVs at these imbalance regions were genotyped in lung cancer-based and COPD-based retrospective studies. Functional assays were conducted to assess function of promising CNVs. A total of 115 genomic imbalances were discovered occurring at a frequency of more than 25%. The CNVR_3425.1, overlapping the chr16q24.1 with genomic imbalance, was significantly associated with increased risks of lung cancer (OR = 1.76; 95% CI = 1.46–2.11) and COPD (OR = 1.98; 95% CI = 1.57–2.51). The increase copy of CNVR_3425.1 forms a new additional truncated FOXF1 adjacent non-coding developmental regulatory RNA (FENDRR) sequences comparing the gene promoter and perturbs the transcriptional factors (TFs) binding to the original FENDRR promoter and further downregulates FENDRR, a long intergenic non-coding RNA (lincRNA) that functions to inhibit lung cancer by affecting expressions of an abundant number of genes, including the tumor suppressor FOXF1. FENDRR can upregulate FOXF1 by competitively binding to miR-424. The TFs early growth response 1 (EGR1) and transcription factor AP-2 alpha (TFAP2A) were further found to involve the CNVR_3425.1-mediated FENDRR dysregulation. These findings suggested the CNVR_3425.1 to be a possibly predictive biomarker for the risk of lung cancer and COPD, and targeted molecular therapy pertaining to FENDRR upregulation may be a valuable pathway to fight two diseases. Introduction In recent years, several association studies especially genome-wide association studies have identified a lot of single nucleotide polymorphisms (SNPs) to be idiotypic risk indicators of human diseases. There have been lofty expectations from these SNPs to improve precision prediction of disease risk, but till now appear to have had limited effect. Robert K. Nam et al. reported that a nomogram incorporating several genome-wide association study (GWAS) SNPs and other host factors only had a middling value in the diagnosis of prostate cancer (1). Qian D.C. et al. also demonstrated that although SNPs contributed a large increase in risk prediction of lung cancer, the value was not high (2). Such inadequacies were also observed for other diseases (3–5). Several reasons have been proposed for this defect, a major of which is ‘missing heritability’ (6). ‘Missing heritability’ refers to those SNPs that cannot account for all of the heritability of diseases on account of other kinds of genetic variant such as copy number variation (CNV) (7,8). Since a person’s susceptibility to disease depends on a combined effect of all disease-associated variants, we still need to put in greater efforts on discovering missing heritability (8–10). Germline copy number variations (gCNV) is one of the major genetic components of missing heritability, which has been recognized as a contributor of cancer risk (11,12). Characterized as loss or gain of stretches of DNA, gCNVs have a greater influence on affecting function of covered genes than SNPs, which harbor great contributions to susceptibility of human disease. Genomic imbalance refers to a genome showing any somatic loss or gain of DNA sequences compared with the reference DNA whole sequence of the genome of interest (13). It is the most frequent event in tumorigenesis and typically drives cancer with alterations in embedded genes (14–16). Relative to genomic imbalance, gCNVs are loss or gain of DNA sequences that may cause effects as similar as genomic imbalance on embedded genes but can be inheritable. Of late, gCNVs have been suggested to be a crucial factor affecting carcinogen-induced genetic imbalance (17,18). On account of those genomic imbalances pinpointing various oncogenes and tumor suppressors including coding and RNA genes, we hypothesized that gCNVs overlapping regions with genomic imbalance of lung cancer contribute to the risk of lung cancer. In accordance with this, we performed array comparative genomic hybridization (aCGH) to identify genomic imbalance regions of lung cancer and conducted a two-stage retrospective study with a total of 2072 lung cancer patients and 2077 normal controls to test associations between common gCNVs in genomic imbalance regions and lung cancer risk. We also analyzed effects of promising gCNVs on the risk of chronic obstructive pulmonary disease (COPD) with a total of 1025 COPD patients and 1061 normal lung function controls because COPD is a possible intermediate phenotype of lung cancer. We further carried out a series of experiments to test functions of promising gCNVs. Materials and methods Study subjects All study subjects have been described in previously published studies (19). In brief, this study totally recruited 1056 lung cancer cases and 1056 healthy controls as well as 1025 COPD patients and 1061 normal lung function controls from Guangzhou region of China and 1016 lung cancer cases and 1021 normal controls as well as 365 COPD cases and 388 normal lung function controls from Suzhou city in China in total. The southern Chinese population was used as a discovery set, and the eastern population was used as a validation set. None of the COPD cases have lung cancer. Detailed information on recruitment of subjects are described in Supplementary Methods, available at Carcinogenesis Online. Meanwhile, 52 pairs of lung cancer tissues and corresponding non-tumor normal tissues were also collected during surgical excision. Each subject had signed a written informed consent. This study was approved by the institutional review boards of Guangzhou Medical University and Soochow University. In addition, the use of mouse was followed along with the institutional review boards of Guangzhou Medical University. Agilent aCGH analysis Genomic imbalances were tested in eight randomly selected pairs of lung cancer tissues and adjacent normal tissues using the Agilent SurePrint G3 human CGH 4 × 180K array supplied by a commercial company (Biotechnology Corporation, Shanghai, China). CNV selection and detection According to gCNVs information of East Asian population (20), there are a total of 58 common gCNVs (altered copy number frequency, ACNF > 10%) that are located in described genomic imbalance regions of lung cancer. We further selected eight promising gCNVs, whose chromosome regions bear well-established oncogene or tumor suppressor. Copy numbers of these gCNVs were first detected in randomly selected 110 pairs of cases and age- and sex-matched controls with the Accucopy assay (12). Then, the promising CNVR_3425.1 was genotyped in all subjects using the Taqman assay. The results were 95.0% concordant between the two assays. Detailed information on genotyping is presented in Supplementary Methods, available at Carcinogenesis Online. Cell culture The human lung cancer cell lines A549 and PC-9 were purchased from Cell Bank of Type Culture Collection of the Chinese Academy of Science (Shanghai Institute of Cell Biology, Shanghai, China). Both cell lines were tested and authenticated by the standard short tandem repeat DNA typing methodology before used in this study. All cells were cultured in RPMI1640 medium (Gibco, life technologies, California) supplemented with 10% fetal bovine serum (FBS). Cells were placed in a CO2 incubator (SANYO Electric Co., Ltd., Japan) with constant 90% humidity and 5% CO2. Gene expression examination On account of that gCNVs may influence expression of embedded genes (21,22), we asked whether expressions of genes residing in CNVR_3425.1 are affected by it. Messenger RNA (mRNA) levels of four coding genes that are forkhead box F1 (FOXF1), methenyltetrahydrofolate synthetase domain containing (MTHFSD), forkhead box C2 (FOXC2) and forkhead box L1 (FOXL1), and two RNA genes that are FOXF1 adjacent non-coding developmental regulatory RNA (FENDRR), FOXC2 antisense RNA 1 (FOXC2-AS1) were examined using the SYBR-Green quantitative real-time PCR (qRT-PCR). Primers for these genes are shown in Supplementary Table S1, available at Carcinogenesis Online. FENDRR promoter luciferase reporter assay Given that the CNVR_3425.1 covers a majority of intron 1, exon 1, 5′-untranlated region (5′-UTR) and upstream promoter of FENDRR, we asked whether this new additional truncated FENDRR sequences perturbs FENDRR transcriptional activity. We constructed luciferase reporters carrying single copy or dual copies of the FENDRR promoter via cloning a 1400 bp fragment (from −1400 to 0 bp relative to transcriptional initiation site) or a 2900 bp fragment comprising double 1400 bp fragments and a connected short sequence of FENDRR 5′-UTR (from 0 to 100 bp relative to transcriptional initiation site) into the pGL3.1 vector because sequence analysis (http://www.genecards.org/cgi-bin/carddisp.pl?gene=FENDRR) showed that the 1400 bp sequences has the basic characteristic of promoter with two concentrated transcription factors’ (TFs’) binding sites (from −1164 to −906 bp and from −471 to −221 bp relative to transcriptional initiation site) in upstream of FENDRR. The luciferase reporters were defined as pGL3.1-1 copy and pGL3.1–2 copies, respectively. We further mutated the pGL3.1–2 copies reporter at the two TFs’ binding sites of the 5′ terminal FENDRR promoter by deleting these sequences (pGL3.1–2 copies truncated). The protocol for in vitro luciferase assay was in accordance with the standard. A concentration gradient from 5, 10, 50, 100, 500 to 1000 ng of pGL3.1 vectors and a 10 ng of referential plasmid namely pRL-TK were co-transfected into two lung cancer cell lines, A549 and PC-9. RNA interference and ChIP We would like to know more about which TFs involving the dysregulation of FENDRR transcription related to the CNVR_3425.1. To reveal TFs with potentials on targeting the FENDRR promoter, RNA interference and chromatin immunoprecipitation (CHIP) assay were used. In experiments of RNA interference, a total of nine small interfering RNAs (siRNAs) were designed and synthesized to target the coding sequences of four predicted TFs toward binding the FENDRR promoter that are early growth response 1 (EGR1), EGR2, SP1 and transcription factor AP-2 alpha (TFAP2A; https://www-bimas.cit.nih.gov/molbio/proscan/). After transfection of 50 nM siRNA, expression levels of these TFs were tested in A549 cells followed by 12 h using the qRT-PCR. Primers for these TFs are shown in Supplementary Table S1, available at Carcinogenesis Online. The effective siRNAs (75 nM) were then co-transfected with the pGL3.1-1 copy or pGL3.1–2 copies vector (500 ng) to show their effects on transcriptional activity of the FENDRR promoter. A siRNA with random sequences transfection was used as a control (Mock). The sequences for siRNAs are shown in Supplementary Table S2, available at Carcinogenesis Online. A siRNA to target the coding sequence of FOXF1 was also synthesized as suggested (23). The ChIP assay was used for mapping the in vivo distribution of TFs associated with the FENDRR promoter following the standard protocol of One-Day ChIP Kits (EZ-Magna ChIP™ A/G, Darmstadt, Germany). Two pairs of primer were designed for PCR analysis. Their sequences are shown in Supplementary Table S3, available at Carcinogenesis Online. Construction of the lentivirus vector of FENDRR The whole cDNA of FENDRR was synthesized and cloned into the lentiviral expression vector pEZ-Lv201 (Genecopoeia Biotech Co. Ltd., Guangzhou, China). The empty pEZ-Lv201 was used as a control. The pEZ-Lv201-FENDRR was further used to construct a lentiviral vector containing truncated version of FENDRR with miR-424 binding site absence by site-directed mutagenesis using the Quick-Change site-directed mutagenesis kit (Stratagene, La Jolla, California). Detailed protocol is presented in Supplementary Methods, available at Carcinogenesis Online. Cell phenotypic experiments The lentiviral vector was used for construction of FENDRR stably overexpressed cells and referential cells. The cell counting kit-8 assay and flow cytometry analysis were performed to measure cell proliferation, cell cycle and apoptosis. The Transwell assay with uncoated or Matrigel-coated (BD Biosciences, California) Boyden chambers was conducted to assess cell migration and invasion. The tablet cloning experiment and soft-agar colony assay were applied for inspecting in vitro clonogenesis ability of cells. The nude mouse tumorigenicity assay was employed to measure the in vivo tumorigenesis abilities of cells. The protocols for above assays are described in Supplementary Methods, available at Carcinogenesis Online. Gene expression profiling analysis The whole human genome oligo microarray was used to assess alteration of gene expression profiles mediated by FENDRR by a commercial company (Biotechnology Corporation, Shanghai, China). Bioinformatics analysis on public records of FENDRR We quested the Cancer Genome Atlas (TCGA) database to validate expressional status of FENDRR in lung cancer and its correlation with cancer survival as well as to explore its related coding genes by constructing FENDRR co-expression network. Available data on FENDRR expression were derived from 979 non-small cell lung cancer (NSCLC) patients in the TCGA data portal (https://portal.gdc.cancer.gov/). The weighted correlation network analysis in R software was used to achieve expression module, and the software Cytoscape was applied for constructing the network diagram. FOXF1 3′-UTR luciferase reporter assay and microRNA mimics treatment Since FOXF1 was found to be regulated by FENDRR, we asked what the regulatory mechanism is. Moran N Cabili et.al. have revealed that FENDRR is localized in both nucleus and cytosol (24), implying a possible role of FENDRR as post-transcriptional regulator such as competing endogenous RNA. We thus performed bioinformatics analysis with the starbase v2.0 website to forecast possible FENDRR-FOXF1-miRNA interactions (http://starbase.sysu.edu.cn/mirLncRNA.php) and found that FENDRR and FOXF1 share binding sites of seven microRNAs including miR-15a, miR-15b, miR-16, miR-195, miR-424, miR-496 and miR-708-5p. The 3′-UTR of FOXF1 was cloning into the Psi-CHECK2 luciferase vector. The mimics of above microRNAs were synthesized and used at a concentration of 50 nM in followed luciferase assay. A microRNA mimics with random sequences transfection was used as a control (Mock). Moreover, the FOXF1 expression was further tested in A549 cells after 8 h followed by transfection of 100 nM miR-424 mimics (6 h) and treatment of 2 mg/L actinomycin D (2 h, Sigma, MO). The proliferation of cells in response to miR-424 treatment (100 nM) or FOXF1 siRNA (75 nM) treatment was also assessed. Statistical analysis Differences in frequency of copy number between cases and controls were evaluated using the McNemar’s χ2 test. Associations between the CNVR_3425.1 and disease risk were tested by the multivariable logistic regression using the ‘PROC logist’ with adjustment for age, sex, pre-existing TB, pack-year smoked, house ventilation, biomass usage and occupational exposure to metallic toxicant, which were characteristic as risk factors of both diseases in our previous study (25). Interaction between risk factors and the CNVR_3425.1 was assessed using the multiplicative interaction. The Breslow–Day test was applied for analyzing whether the results were homogenous between stratified ORs. The Log-rank test and Cox model with adjustment for age, sex, smoking status histological types and clinical stages were used to evaluate the effect of FENDRR expression on lung cancer survival. Study power was calculated using the PS Software. Differences in numeric data were tested with the Student’s t-test or the one-way ANOVA test. Differences in gene expression between cancer tissues and normal ones were assessed by the paired t-test. Correlation between FENDRR and FOXF1 was tested by the Pearson correlation analysis. All tests were two-sided using the SAS software (version 9.3; SAS Institute, Cary, North Carolina). P < 0.05 was considered to be statistically significant. Results Characterization of genomic imbalances in lung cancer The aCGH analysis detected a total of 325 regions with genomic imbalance, ranging from focal rearrangements (70 kb–5 Mb) to chromosome-arm alterations with chromosomal segments amplification or deletion (Figure 1a). The detailed data has been submitted to the GEO database (GSE89927: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89927). Among these mutations, a total of 115 regions occurred at a frequency of more than 25% (Supplementary Table S4, available at Carcinogenesis Online). Figure 1. View largeDownload slide Genomic imbalance regions of lung cancer including chr16q24.1 and genotyping of the CNVR_3425.1. (a) The Circos for visualization of the aCGH array-discovered 325 genomic imbalance regions of lung cancer in human genome. Blue irregular cords refer to chromosomal segment amplification and jacinth irregular cords refer to chromosomal segment loss. (b) The aCGH array showing copy number gain or loss of chr16 (up) and genomic imbalances occurring at chr16q23.1–24.3 (below). Red dot implies loss and blue dot implies gain at the genomic site. (c) Chromosome location of the CNVR_3425.1 and genes residing in it. (d) The Accucopy assay was performed to determine copy number of the CNVR_3425.1. Bar height corresponds to the mean for two different probes. (e) The Taqman assay was conducted to measure copy number of the CNVR_3425.1. Bar height corresponds to the mean, and error bars represent SD for three technical replicates. Figure 1. View largeDownload slide Genomic imbalance regions of lung cancer including chr16q24.1 and genotyping of the CNVR_3425.1. (a) The Circos for visualization of the aCGH array-discovered 325 genomic imbalance regions of lung cancer in human genome. Blue irregular cords refer to chromosomal segment amplification and jacinth irregular cords refer to chromosomal segment loss. (b) The aCGH array showing copy number gain or loss of chr16 (up) and genomic imbalances occurring at chr16q23.1–24.3 (below). Red dot implies loss and blue dot implies gain at the genomic site. (c) Chromosome location of the CNVR_3425.1 and genes residing in it. (d) The Accucopy assay was performed to determine copy number of the CNVR_3425.1. Bar height corresponds to the mean for two different probes. (e) The Taqman assay was conducted to measure copy number of the CNVR_3425.1. Bar height corresponds to the mean, and error bars represent SD for three technical replicates. gCNVs in genomic imbalances and lung cancer risk Eight common gCNVs located in above 115 regions were selected to genotype with regard to well-established tumor-related genes residing in their regions. They are CNVR_1167.2 at Chr5p15.33, CNVR_1215.1 at Chr13.3, CNVR_1372.1 at Chr5q35.3, CNVR_1994.1 at 8q24.3, CNVR_3339.1 at 16p11.2, CNVR_3367.1 at 16q13, CNVR_3425.1 at 16q24.1 and CNVR_3916.1 at 20q13.13. Figure 1b exhibits one example of region with genomic imbalance at chr16q24.1, and Figure 1c demonstrates chromosome location of the CNVR_3425.1 and its embedded genes. Results from the Accucopy assay showed that the frequency distribution of copy number of CNVR_3425.1 at chr16q24.1 was significantly different between lung cancer cases and matched controls (P = 0.014; Table 1). No significant deviation was observed for other gCNVs (P > 0.05 for all; Table 1). Genotyping of the CNVR_3425.1 by the Accucopy assay is presented in Figure 1d and those of other ones in Supplementary Figure S1, available at Carcinogenesis Online. Table 1. Calculated copy number of candidate gCNVs in lung cancer cases and controls in the discovery phase CNV  Chromosome locationa  Cytobanda  Residing genes  Variation typeb  Reported frequencyb  Case (N = 110)  Control (N = 110)  P-value*  Copy numbercN (%)  Copy numbercN (%)  CNVR_1167.2  Chr5: 801 638–878 490  5p15.33  ZDHHC11  Gain  13.3%  87 (79.1)/23 (20.9)  91 (82.7)/19 (17.3)  0.450  CNVR_1215.1  Chr5: 32 142 258–32 203 868  5p13.3  PDZD2, GOLPH3  Gain  10%  94 (85.5)/16 (14.5)  99 (90.0)/11 (10.0)  0.317  CNVR_1372.1  Chr5:179 927 364–179 927 831  5q35.3  CNOT6  Loss  6.7%  104 (94.5)/6(5.5)  103 (93.6)/7(6.4)  0.763  CNVR_1994.1  Chr8:145 694 587–145 728 609  8q24.3  PPP1R16A, GPT, MFSD3, RECQL4, LRRC14, LRRC24, MGC70857, KIAA1688, CR612338, C8orf82, 09457, AK094577  Gain  6.7%  101 (91.8)/9 (8.2)  106 (96.4)/4 (3.6)  0.132  CNVR_3339.1  Chr16: 28 515 895–28 534 480  16p11.2  SULT1A1  Loss  33.3%  85 (77.3)/25 (22.7)  74 (67.3)/36 (32.7)  0.086  CNVR_3367.1  Chr16:55 256 326–55 267 342  16q13  MT1G, MT1H, MT1IP  Gain/loss  13.3%  7 (6.4)/91 (82.7)/12 (10.9)  5 (4.5)/96 (87.3)/9 (8.2)  0.317  CNVR_3425.1  Chr16:85 074 275–85 178 636  16q24.1  FOXF1, MTHFSD, FOXC2, FOXL1, FENDRR, FOXC2-AS1  Gain  6.7%  88 (80.0)/22 (22.0)  100 (90.9)/10 (9.1)  0.014  CNVR_3916.1  Chr20:48 238 984–48 243 094  20q13.13  CEBPB, CEBPB-AS1  Gain  10%  90 (81.8)/20 (18.2)  98 (89.1)/12 (10.9)  0.114  CNV  Chromosome locationa  Cytobanda  Residing genes  Variation typeb  Reported frequencyb  Case (N = 110)  Control (N = 110)  P-value*  Copy numbercN (%)  Copy numbercN (%)  CNVR_1167.2  Chr5: 801 638–878 490  5p15.33  ZDHHC11  Gain  13.3%  87 (79.1)/23 (20.9)  91 (82.7)/19 (17.3)  0.450  CNVR_1215.1  Chr5: 32 142 258–32 203 868  5p13.3  PDZD2, GOLPH3  Gain  10%  94 (85.5)/16 (14.5)  99 (90.0)/11 (10.0)  0.317  CNVR_1372.1  Chr5:179 927 364–179 927 831  5q35.3  CNOT6  Loss  6.7%  104 (94.5)/6(5.5)  103 (93.6)/7(6.4)  0.763  CNVR_1994.1  Chr8:145 694 587–145 728 609  8q24.3  PPP1R16A, GPT, MFSD3, RECQL4, LRRC14, LRRC24, MGC70857, KIAA1688, CR612338, C8orf82, 09457, AK094577  Gain  6.7%  101 (91.8)/9 (8.2)  106 (96.4)/4 (3.6)  0.132  CNVR_3339.1  Chr16: 28 515 895–28 534 480  16p11.2  SULT1A1  Loss  33.3%  85 (77.3)/25 (22.7)  74 (67.3)/36 (32.7)  0.086  CNVR_3367.1  Chr16:55 256 326–55 267 342  16q13  MT1G, MT1H, MT1IP  Gain/loss  13.3%  7 (6.4)/91 (82.7)/12 (10.9)  5 (4.5)/96 (87.3)/9 (8.2)  0.317  CNVR_3425.1  Chr16:85 074 275–85 178 636  16q24.1  FOXF1, MTHFSD, FOXC2, FOXL1, FENDRR, FOXC2-AS1  Gain  6.7%  88 (80.0)/22 (22.0)  100 (90.9)/10 (9.1)  0.014  CNVR_3916.1  Chr20:48 238 984–48 243 094  20q13.13  CEBPB, CEBPB-AS1  Gain  10%  90 (81.8)/20 (18.2)  98 (89.1)/12 (10.9)  0.114  aReferenced by the UCSC with NCBI36/hg18 database (http://genome.ucsc.edu/). bInformation reported by Park H et al. (20). cCopy number is exhibited as 2-copy/≥3-copy for gain-type CNV, 2-copy/≤1-copy for loss-type CNV, and ≤1-copy/2-copy/≥3-copy for gain/loss-type CNV. *P-value calculated by the McNemar’s χ2 test for matched case-control data. View Large CNVR_3425.1 increases the risk of lung cancer and COPD Distributions of demographic variables, selected risk factors and clinical features of all studied samples are shown in Supplementary Table S5, available at Carcinogenesis Online and have been described elsewhere (19,26). We only genotyped the CNVR_3425.1 with promising significance and detected more than three genotypes of copy number using the Taqman assay (Figure 1e), including common 2-copy genotype and more than 2-copy genotype that were described as ≥3 copy. As shown in Table 2, compared with the 2-copy carriers, these ≥3-copy ones harbored a significantly increase in risk for developing lung cancer by 70% (OR = 1.70; 95% CI = 1.33–2.18; P = 2.75 × 10–5) in southern Chinese. Data from the eastern Chinese population further confirmed this observation. The ≥3-copy contributed a significantly higher risk to lung cancer than the 2-copy (OR = 1.85; 95% CI = 1.40–2.43; P = 2.75 × 10–5). When merged the two populations, the ≥3-copy predisposed carriers for developing lung cancer when compared with the 2-copy (OR = 1.76; 95% CI = 1.46–2.11; P = 1.86 × 10–9). Moreover, the ≥3-copy consistently increased the risk of COPD by 116, 74 and 98% when compared with the 2-copy in the southern Chinese (OR = 2.09; 95% CI = 1.59–2.76; P = 1.50 × 10–7), eastern Chinese (OR = 1.74; 95% CI = 1.11–2.72; P = 0.015) and merged populations (OR = 1.98; 95%CI = 1.57–2.51; P = 1.28 × 10–8), respectively. Table 2. Association between the CNVR_3425.1 and the risk of lung cancer and COPD in Chinese Copy number  Cases N (%)a  Controls N (%)a  Crude OR (95% CI)  Adjusted OR (95% CI)b  Lung cancer  Southern Chinese (discovery set)  1056  1056      2-copy  849 (81.0)  919 (87.9)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  199 (19.0)  127 (12.1)  1.70 (1.33–2.16)  1.70 (1.33–2.18)  Eastern Chinese (validation set)  1016  1021      2-copy  842 (83.8)  918 (90.6)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  163 (16.2)  95 (9.4)  1.87 (1.43–2.45)  1.85 (1.40–2.43)  Merged population  2072  2077      2-copy  1691 (82.4)  1837 (89.2)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  362 (17.6)  222 (10.8)  1.77 (1.48–2.12)  1.76 (1.46–2.11)  COPD          Southern Chinese (discovery set)          2-copy  850 (83.3)  958 (91.0)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  170 (16.7)  95 (9.0)  2.02 (1.54–2.64)  2.09 (1.59–2.76)  Eastern Chinese (validation set)  365  388      2-copy  290 (83.8)  326 (89.8)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  56 (16.2)  37 (10.2)  1.70 (1.09–2.65)  1.74 (1.11–2.72)  Merged population          2-copy  1140 (83.5)  1284 (90.7)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  226 (16.5)  132 (9.3)  1.93 (1.53–2.43)  1.98 (1.57–2.51)  Copy number  Cases N (%)a  Controls N (%)a  Crude OR (95% CI)  Adjusted OR (95% CI)b  Lung cancer  Southern Chinese (discovery set)  1056  1056      2-copy  849 (81.0)  919 (87.9)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  199 (19.0)  127 (12.1)  1.70 (1.33–2.16)  1.70 (1.33–2.18)  Eastern Chinese (validation set)  1016  1021      2-copy  842 (83.8)  918 (90.6)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  163 (16.2)  95 (9.4)  1.87 (1.43–2.45)  1.85 (1.40–2.43)  Merged population  2072  2077      2-copy  1691 (82.4)  1837 (89.2)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  362 (17.6)  222 (10.8)  1.77 (1.48–2.12)  1.76 (1.46–2.11)  COPD          Southern Chinese (discovery set)          2-copy  850 (83.3)  958 (91.0)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  170 (16.7)  95 (9.0)  2.02 (1.54–2.64)  2.09 (1.59–2.76)  Eastern Chinese (validation set)  365  388      2-copy  290 (83.8)  326 (89.8)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  56 (16.2)  37 (10.2)  1.70 (1.09–2.65)  1.74 (1.11–2.72)  Merged population          2-copy  1140 (83.5)  1284 (90.7)  1.00 (ref.)  1.00 (ref.)  ≥3-copy  226 (16.5)  132 (9.3)  1.93 (1.53–2.43)  1.98 (1.57–2.51)  aAbout 1.48% of all samples were failed to determine copy number due to the DNA quality or unknown reason. bAdjusted in a logistic regression model that included age, sex, pre-existing TB, pack-year smoked, house ventilation, biomass usage and occupational exposure to metallic toxicant. View Large In addition, stratification analysis showed no significant difference in the association between CNVR_3425.1 and the risk of lung cancer as well as COPD (Supplementary Table S6 and S7, available at Carcinogenesis Online). Also, no significant interaction was observed between any surrounding factors and the gCNV (Supplementary Table S6 and S7, available at Carcinogenesis Online). CNVR_3425.1 significantly affects expression of FENDRR but not other embedded genes The gene expression test showed that the lung cancer tissues carrying the ≥3-copy of CNVR_3425.1 (n = 22) exerted a significantly lower expression of FENDRR than those carrying the 2-copy (n = 30; mean ± standard deviation: 0.028 ± 0.043 versus 0.052 ± 0.037; P = 0.037; Figure 2a). The ≥3-copy was also correlated with a decreased expression of FOXF1 in comparison with the 2-copy (0.605 ± 0.591 versus 0.968 ± 0.943). However, the difference was not significant (P = 0.119; Figure 2a). Furthermore, such an effect was not observed for other genes (P > 0.05 for all; Supplementary Figure S2, available at Carcinogenesis Online). Figure 2. View largeDownload slide Biological effect of the CNVR_3425.1 on FENDRR expression as well as FOXF1 expression. (a) The qRT-PCR was performed to assess the expression of FENDRR (left) as well as FOXF1 (right) in lung tissues carrying different copy of the CNVR_3425.1. P-values are calculated by the Student’s t-test. (b) Schematic of the reporter genes containing the 1-copy or 2-copy or 2-copy truncated version of FENDRR promoter included in the CNVR_3425.1. TIS: transcriptional initiation site. TFs’ binding site 1: from −1164 to −906 bp relative to TIS; TFs’ binding site 2: from −471 to −221 bp relative to TIS. (c) The luciferase assay was used to test differences in transcriptional activity between pGL3.1 reporters carrying the different copy versions of FENDRR promoter in conventional A549 cells (left) and PC-9 cells (right). The firefly luciferase was used to show transcriptional activity of the FENDRR promoter, and the renilla luciferase was used as the internal standard (i.e., Fluc/Rluc). *P < 0.05, calculated by the one-way ANOVA test. (d) The luciferase assay was used to measure the transcriptional activity of FENDRR promoter (pGL3.1-1 copy) in response to TFs’ siRNAs treatment in conventional A549 cells. *P < 0.05, calculated by the Student’s t-test. (e) The luciferase assay was conducted to measure the transcriptional activity between pGL3.1 reporters carrying the different copy versions of FENDRR promoter in response to treatments of EGR1 siRNAs or TFAP2A siRNAs in conventional A549 cells. f. The ChIP was performed to analyze EGR1 (left) and TFAP2A (right) recruitment at selected loci (−1163 to −016 bp relative to TIS) of the FENDRR promoter in conventional A549 cells. Input was used as a genomic DNA-based positive control, GAPDH was used as a ChIp-based positive control, IgG was used as a negative control and H2O was used as a blank control. Bar height corresponds to the mean, and error bars represent SD for three technical replicates. Figure 2. View largeDownload slide Biological effect of the CNVR_3425.1 on FENDRR expression as well as FOXF1 expression. (a) The qRT-PCR was performed to assess the expression of FENDRR (left) as well as FOXF1 (right) in lung tissues carrying different copy of the CNVR_3425.1. P-values are calculated by the Student’s t-test. (b) Schematic of the reporter genes containing the 1-copy or 2-copy or 2-copy truncated version of FENDRR promoter included in the CNVR_3425.1. TIS: transcriptional initiation site. TFs’ binding site 1: from −1164 to −906 bp relative to TIS; TFs’ binding site 2: from −471 to −221 bp relative to TIS. (c) The luciferase assay was used to test differences in transcriptional activity between pGL3.1 reporters carrying the different copy versions of FENDRR promoter in conventional A549 cells (left) and PC-9 cells (right). The firefly luciferase was used to show transcriptional activity of the FENDRR promoter, and the renilla luciferase was used as the internal standard (i.e., Fluc/Rluc). *P < 0.05, calculated by the one-way ANOVA test. (d) The luciferase assay was used to measure the transcriptional activity of FENDRR promoter (pGL3.1-1 copy) in response to TFs’ siRNAs treatment in conventional A549 cells. *P < 0.05, calculated by the Student’s t-test. (e) The luciferase assay was conducted to measure the transcriptional activity between pGL3.1 reporters carrying the different copy versions of FENDRR promoter in response to treatments of EGR1 siRNAs or TFAP2A siRNAs in conventional A549 cells. f. The ChIP was performed to analyze EGR1 (left) and TFAP2A (right) recruitment at selected loci (−1163 to −016 bp relative to TIS) of the FENDRR promoter in conventional A549 cells. Input was used as a genomic DNA-based positive control, GAPDH was used as a ChIp-based positive control, IgG was used as a negative control and H2O was used as a blank control. Bar height corresponds to the mean, and error bars represent SD for three technical replicates. CNVR_3425.1 influences the transcriptional efficiency of FENDRR promoter The construction features of three reporters comprising the different copies of FENDRR promoter are shown in Figure 2b. Only at high concentrations of reporter genes (≥50 ng), the duplicated copies of FENDRR promoter showed significantly weaker luciferase activity when compared with the single copy of FENDRR promoter in both A549 and PC-9 cells, while as expected, the duplicated copies of truncated FENDRR promoter, missing two predicted TFs’ binding sites, exerted approaching luciferase activity in comparison with the single copy (Figure 2c). At low concentrations, no significant difference was observed between the three types of reporter genes. Knockdown of EGR1 and TFAP2A inhibits activity of the FENDRR promoter Since almost all siRNAs showed reduced effects on target genes, we selected two siRNAs for each gene according to the degradation effectivity (Supplementary Figure S3a–d, available at Carcinogenesis Online). The luciferase assay further revealed that knockdown of EGR1 or TFAP2A by any one siRNA showed consistently decreased transcriptional activity in comparison with mock transfection (P < 0.05 for all; Figure 2d). Moreover, following co-transfection of the EGR1/TFAP2A siRNAs and the pGL3.1-1 copy or pGL3.1–2 copies’ reporters into A549 cells, these siRNAs diminished the difference of luciferase activity between the two reporters (Figure 2e). The ChIP assay further confirmed that both EGR1 and TFAP2A bound to the FENDRR promoter (Figure 2f, Supplementary Figure S3e, available at Carcinogenesis Online), but EGR2 and SP1 did not (Supplementary Figure S3f and g, available at Carcinogenesis Online). FENDRR expression was correlated with lung cancer survival With the TCGA data, we stratified the FENDRR expression by median level of lung cancer tissues. Expressed level equal or greater than median was defined as high, while less than median was defined as low. High expressed FENDRR conferred longer survival time (1790 days versus 1346 days, Log-rank test P = 0.088) and lower fatality rate (hazard ratio = 0.50, 95% CI = 0.25–0.98) when compared with low expressed FENDRR. FENDRR functions to inhibit tumor growth FENDRR was downregulated in 76.9% (40/52) cancer tissues when compared with normal tissues (P = 0.025; Figure 3a). The TCGA data further confirmed that FENDRR expressed a significantly lower level in lung cancer tissues than their normal counterparts in both lung adenocarcinoma (log2(FoldChange) = −3.981, P= 1.98 × 10−80) and lung squamous cell carcinoma (log2(FoldChange) = −4.079, P= 2.92 × 10−71). Additionally, FENDRR was almost undetectable in all lung cancer cell lines. Having established overexpression of FENDRR in transfected A549 and PC-9 cells, we examined effects of FENDRR on phenotypes of lung cancer cells. We found that FENDRR overexpression well reduced the rate of cell proliferation at levels beginning with 1000 cells per well and 500 cells per well in both cells (Figure 3b). Overexpression of FENDRR led more A549 cells to rest on G0–G1 phase and less PC-9 cells to keep in G2–M phase (P < 0.05 for both; Figure 3c). However, overexpression of FENDRR exerted no significant effect on cell apoptosis (P > 0.05 for both; Figure 3d). Furthermore, FENDRR overexpression resulted in sharply declined tumor growth in vitro (P < 0.05 for all; Figure 3e and f). The A549 and PC-9 cells with high expression of FENDRR exhibited significantly decreased growth rate of tumor xenograft compared with their controls in vitro (Figure 4a–c). Figure 3. View largeDownload slide FENDRR functions to inhibit lung tumor growth in vitro. Both A549 and PC-9 cells were transfected with pEZ-Lv201-FENDRR and pEZ-Lv201-Empty. (a) The qRT-PCR was performed to assess the expression of FENDRR in 52 pairs of lung cancer tissues and corresponding non-tumor normal tissues. Bar height corresponds to the log mean difference between tumor tissues and normal tissues from three technical replicates. P-value is calculated by the paired t-test. (b) The cell counting kit-8 assay was conducted to determine the proliferation of A549 and PC-9 cells with concentration of 500 cells per well and 1000 cells per well. Circle dot on line corresponds to the mean; error bars represent SD for seven biological replicates. (c) The flow cytometry was conducted to determine the cell cycle of A549 and PC-9 cells. (d) The flow cytometry was conducted to determine the cell apoptosis of A549 and PC-9 cells. Bar height corresponds to the mean, and error bars represent SD for three biological replicates. (e) and (f) The plate colony (e) and soft-agar experiments (f) were conducted to test for tumorigenicity of A549 and PC-9 cells. Bar height corresponds to the mean, and error bars represent SD for six biological replicates. *P < 0.05, **P < 0.01, ***P < 0.001, calculated by the Student’s t-test. Figure 3. View largeDownload slide FENDRR functions to inhibit lung tumor growth in vitro. Both A549 and PC-9 cells were transfected with pEZ-Lv201-FENDRR and pEZ-Lv201-Empty. (a) The qRT-PCR was performed to assess the expression of FENDRR in 52 pairs of lung cancer tissues and corresponding non-tumor normal tissues. Bar height corresponds to the log mean difference between tumor tissues and normal tissues from three technical replicates. P-value is calculated by the paired t-test. (b) The cell counting kit-8 assay was conducted to determine the proliferation of A549 and PC-9 cells with concentration of 500 cells per well and 1000 cells per well. Circle dot on line corresponds to the mean; error bars represent SD for seven biological replicates. (c) The flow cytometry was conducted to determine the cell cycle of A549 and PC-9 cells. (d) The flow cytometry was conducted to determine the cell apoptosis of A549 and PC-9 cells. Bar height corresponds to the mean, and error bars represent SD for three biological replicates. (e) and (f) The plate colony (e) and soft-agar experiments (f) were conducted to test for tumorigenicity of A549 and PC-9 cells. Bar height corresponds to the mean, and error bars represent SD for six biological replicates. *P < 0.05, **P < 0.01, ***P < 0.001, calculated by the Student’s t-test. Figure 4. View largeDownload slide FENDRR functions to inhibit lung tumor growth in vivo. (a) The BALB/c nude mice were used to determine the growth of tumor, originating injection of A549 and PC-9 cells. Xenografted tumors originating from subcutaneously implanted A549 (up) and PC-9 (below) cells were established. (b) (A549 cells) and (c) (PC-9 cells) Tumor volume was measured every 3 days, until the end of 3rd week (up). Extracted tumor tissues from the mouse were imaged (left bottom) and weighted (right bottom). Circle dot on line or bar height corresponds to the mean; error bars represent SD for six biological replicates. *P < 0.05, **P < 0.01, calculated by the Student’s t-test. Figure 4. View largeDownload slide FENDRR functions to inhibit lung tumor growth in vivo. (a) The BALB/c nude mice were used to determine the growth of tumor, originating injection of A549 and PC-9 cells. Xenografted tumors originating from subcutaneously implanted A549 (up) and PC-9 (below) cells were established. (b) (A549 cells) and (c) (PC-9 cells) Tumor volume was measured every 3 days, until the end of 3rd week (up). Extracted tumor tissues from the mouse were imaged (left bottom) and weighted (right bottom). Circle dot on line or bar height corresponds to the mean; error bars represent SD for six biological replicates. *P < 0.05, **P < 0.01, calculated by the Student’s t-test. Xu TP et al. have reported that FENDER overexpression suppressed invasion and migration of gastric cancer cells, which are features of tumor progression (27). However, as shown in Supplementary Figure S4, available at Carcinogenesis Online, FENDER overexpression did not significantly inhibit both migration and invasion of A549 and PC-9 cells (P > 0.05 for all). FENDRR drives alteration of cell expression profile The gene expression profiling analysis identified a total of 559 differentially expressed transcripts (|log2(FoldChange)| > 2) in response to FENDRR overexpression, including 359 downregulated transcripts and 200 upregulated ones (Figure 5a). The chip data have been submitted to the GEO database (GSE89828: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE89828). Figure 5. View largeDownload slide FENDRR upregulates FOXF1 and affects a series of gene expressions. (a) The whole human genome oligo microarray was conducted to determine alteration of gene expression profiles in response to FENDRR overexpression in FENDRR overexpressed A549 cells and referential A549 cells. Red dot marks upregulated genes, and green dot marks downregulated genes. (b) Construction of co-expression network according to expressional correlations between FENDRR and related genes based on TCGA data. (c) The qRT-PCR was conducted to determine the FOXF1 mRNA expression. *P < 0.05, **P < 0.01, calculated by the Student’s t-test. (d) The correlation between the FENDRR expression and the FOXF1 expression in lung tissues. Purple dot corresponds to log(FENDRR) expression in x-axis and log(FOXF1) expression in y-axis. The values of r and P are calculated by the Pearson correlation analysis. (e) The qRT-PCR was performed to assess the expression of FOXF1 in 52 pairs of lung cancer tissues and corresponding non-tumor normal tissues. Bar height corresponds to the log mean difference between tumor tissues and normal tissues from three technical replicates. P-value is calculated by the paired t-test. (f) The luciferase assay was used to test transcriptional activity of FOXF1 3′-UTR in conventional A549 cells and PC-9 cells in response to microRNA mimics treatment. The renilla luciferase was used to show transcriptional activity of the FENDRR promoter, and the firefly luciferase was used as the internal standard (i.e., Rluc/Fluc). (g) The luciferase assay was used to test transcriptional activity of FOXF1 3′-UTR in A549 cells overexpressed FENDRR, A549 cells overexpressed truncated FENDRR and A549 referential cells in response to miR-424 mimics treatment. (h) Expression of FOXF1 in A549 cells overexpressed FENDRR, A549 cells overexpressed truncated FENDRR and A549 referential cells in response to treatment of miR-424 mimics. Bar height corresponds to the mean; error bars represent SD for three biological or technical replicates. P-values are calculated by the Student’s t-test. Figure 5. View largeDownload slide FENDRR upregulates FOXF1 and affects a series of gene expressions. (a) The whole human genome oligo microarray was conducted to determine alteration of gene expression profiles in response to FENDRR overexpression in FENDRR overexpressed A549 cells and referential A549 cells. Red dot marks upregulated genes, and green dot marks downregulated genes. (b) Construction of co-expression network according to expressional correlations between FENDRR and related genes based on TCGA data. (c) The qRT-PCR was conducted to determine the FOXF1 mRNA expression. *P < 0.05, **P < 0.01, calculated by the Student’s t-test. (d) The correlation between the FENDRR expression and the FOXF1 expression in lung tissues. Purple dot corresponds to log(FENDRR) expression in x-axis and log(FOXF1) expression in y-axis. The values of r and P are calculated by the Pearson correlation analysis. (e) The qRT-PCR was performed to assess the expression of FOXF1 in 52 pairs of lung cancer tissues and corresponding non-tumor normal tissues. Bar height corresponds to the log mean difference between tumor tissues and normal tissues from three technical replicates. P-value is calculated by the paired t-test. (f) The luciferase assay was used to test transcriptional activity of FOXF1 3′-UTR in conventional A549 cells and PC-9 cells in response to microRNA mimics treatment. The renilla luciferase was used to show transcriptional activity of the FENDRR promoter, and the firefly luciferase was used as the internal standard (i.e., Rluc/Fluc). (g) The luciferase assay was used to test transcriptional activity of FOXF1 3′-UTR in A549 cells overexpressed FENDRR, A549 cells overexpressed truncated FENDRR and A549 referential cells in response to miR-424 mimics treatment. (h) Expression of FOXF1 in A549 cells overexpressed FENDRR, A549 cells overexpressed truncated FENDRR and A549 referential cells in response to treatment of miR-424 mimics. Bar height corresponds to the mean; error bars represent SD for three biological or technical replicates. P-values are calculated by the Student’s t-test. Gene co-expression network The weighted correlation network analysis result indicated that FENDRR expression was significantly correlated with expressions of 407 coding genes in lung adenocarcinoma and 40 ones in squamous cell carcinoma, of which 31 genes were common (Figure 5b). The 31 genes were also mostly revealed by the microarray, including FENDRR neighboring gene FOXF1. FENDRR upregulates FOXF1 expression via competitively binding to miR-424 The detection of expression showed that FENDRR overexpression upregulated FOXF1 expression (Figure 5c), and their expressions were positively correlated in lung tissues (r = 0.877, P = 5.56 × 10–34; Figure 5d). FOXF1 was also downregulated in lung cancer tissues when compared with adjacent lung normal tissues with a clear tendency to significance (P = 0.099; Figure 5e). The luciferase assay further showed that only miR-424 exerted a significant decrease in luciferase activity of FOXF1 3′-UTR in both A549 and PC-9 cells. Such an effect was not observed for other microRNAs (Figure 5f). The miR-424 mimics caused a significantly less decrease in luciferase activity in A549 cells overexpressed FENDRR (16.9%) than that overexpressed truncated FENDRR (27.7%) and referential cells (33.1%; P < 0.001; Figure 5g). Moreover, after treatment of the miR-424 mimics, the FOXF1 expression in A549 cells overexpressed FENDRR had a less decrease (21.8%) as compared with referential cells (37.7%) with a considerable trend toward significance (P = 0.058), while that in A549 cells overexpressed truncated FENDRR exerted a similar decrease (35.4%) as compared with referential cells (P = 0.810; Figure 5h). In addition, following transfection of miR-424 mimics or FOXF1 siRNA, the FOXF1 siRNA induced a significant increase in proliferation rate, whereas miR-424 mimics did not in comparison with control cells as the cell counting kit assay and the tablet cloning experiment shown (Supplementary Figure S5, available at Carcinogenesis Online). Discussion Our knowledge about roles of gCNVs on disease susceptibility and their affinity mechanisms remains limited. Few studies have reported a limited number of gCNVs to be susceptible loci for different cancers, most of which were based on candidate gene strategy and lack of functional evidences. In the current study, we detected the gCNVs in genomic imbalances of lung cancer and identified the CNVR_3425.1 to be a risk indicator of lung cancer and COPD for Chinese populations. We also explicated the mechanism how the gCNV confer susceptibility of both diseases. To the best of our knowledge, this is the first study to investigate on gCNVs in genomic imbalances and a revelation of long intergenic non-coding RNA (lincRNA)-related gCNV. In this post-GWAS era, gCNV is increasingly drawing attention with respect to their contributions on missing heritability of disease. For lung cancer, several gCNVs that are associated with lung cancer risk have been demonstrated, which highlight the gCNVs as important components of cancer heritability (12,28,29). However, due to the limitations of the candidate gene strategy that focused on gCNVs overlapping well-known cancer-related coding genes, relationships between most of gCNVs and human disease are unknown and are yet to be elucidated. Under the prevailing condition that cannot implement large-scale gCNV detection, our study chose a low thoroughput technology to test eight promising gCNVs in chromosome regions with genomic imbalance of lung cancer. We identified one gCNV namely CNVR_3425.1 in the region chr16q23.1–24.3 with contribution on increasing the risk of lung cancer and COPD in Chinese populations. Coincidentally, a report based on the SNP genotyping chip reported that the risk gCNVs of lung cancer are located on genomic recombination hotspots (28). Also, the genetic mutations of chr16q23.1–24.3 have been reported to be implicated in lung developmental disorder and lung cancer (25,30). These results highlight the importance of gCNVs in genomic imbalances, and broader studies testing more such gCNVs are warranted. Several gCNVs have been supposed to affect disease predisposition via their modulatory effects on embedded genes. Here, we proved that the incremental copy of CNVR_3425.1 exerted a decreased expression of FENDRR. Functional mechanisms of gCNV have been documented as gene dosage effect (21,31), distal enhancer regulation (32,33) and structural mutation induction (34). Here, due to the negative correlation and superimposed pattern between the gCNV and FENDRR, mapping the increased copy of CNVR_3425.1 and decreased FENDRR is an involved problem. Using the luciferase assay, we have identified that the increased copy of CNVR_3425.1 weakens transcriptional efficiency of FENDRR because the 2-copy luciferase reporter showed reduced transcriptional activity in comparison with the 1-copy one, and the 2-copy truncated one indeed rescued the activity, which exerted under specific condition with high concentrations of reporter gene. After determining the responsible TFs EGR1 and TFAP2A, we further found that knockdown of EGR1 and TFAP2A diminished the difference of luciferase activity between the 1-copy reporter and the 2-copy one. The findings prompt us to speculate that the incremental copy-formed promoter can disturb the TF’s binding to the original FENDRR promoter, which depends on low responsible TFs’ expression level or great transcriptional demand. When such TFs reduce expression, which might be, for example, induced by carcinogens stimulus, the detrimental function of CNVR_3425.1 is starting to appear. Consistently, downregulation of TFAP2A was observed to promote lung carcinogenesis in response to cigarette smoke condensate (35). Absent EGR1 was also observed in lung cancer tissues (36). Furthermore, one previous study has documented a long-range regulation of FENDRR promoter depending on physical interaction as chromatin looping between the region that FENDRR and CNVR_3425.1 overlapped and a putative distant regulatory region (DRR) (37). The DRR is located in downstream of FENDRR, which exerted an in trans regulation on the FENDRR promoter. These distal transacting factors might have interaction with local factors such as EGR1. Considering the large scale, CNVR_3425.1 would most probably perturb the physically interaction between the DRR and FENDRR promoter, then disrurbing the TF’s binding to the FENDRR promoter and ultimately leading to decreased FENDRR. However, to further determine this perturbation of large gCNV-mediated chromatin, interactions seem to be impossible in contemporary condition and need to be solved in the future. Further analyses supported a tumor suppressor role of FENDRR in lung cancer. FENDRR is a well-established lincRNA (38), whose absence has been found in lung and gastric tumors, resulting defects in lung and gastrointestinal tract (27,39,40). Yet, its concrete role on lung tumorigenesis remains an enigma. Here, both in vivo and in vitro experiments showed that FENDRR was downregulated in lung tumor tissues, and high expression of FENDRR was correlated with favorite lung cancer survival. Overexpression of it markedly inhibited cell proliferation and tumor growth. All these demonstrably indicated that FENDRR functions to inhibit lung cancer development. Moreover, we have validated FOXF1 to be a target of FENDRR in lung cancer, which is firstly reported to be a target of FENDRR in pluripotent cells (41). FOXF1 is located in close proximity to FENDRR and has been identified to be a tumor suppressor (42). Both our expressional results and TCGA data revealed a significantly positive correlation between FENDRR and FOXF1. Also, knockdown of FOXF1 returned FENDRR-mediated phenotype change. Even it has been known that FENDRR can interact with polycomb repressive complex 2 to facilitate promoter methylation and cause decreased target genes (43), which cannot explain why FENDRR upregulates FOXF1. One computational study has documented possible mechanism on FENDRR regulation of FOXF1 based on bioinformatics analysis (44); yet, the projection lacks experimental evidences. Using the luciferase assay, we have revealed that when presented as a competing endogenous RNA, FENDRR can sponge miR-424, reduce its binding to FOXF1 and then upregulate FOXF1. Consistently, downregulation of miR-424 has been report to act as a cancer suppressor involving lung cancer (45). However, may be due to highly endogenous expression in A549 cells (46), the miR-424 mimics did not rescue the FENDRR-mediated decline of cell proliferation. Noticeably, the increased copy of CNVR_3425.1 tends to exert a decreased expression of FOXF1. This is contradictory to the approbable opinion of gene dosage effect, which should have caused upregulated FOXF1, considering copy increase of CNVR_3425.1 duplicates the FOXF1 gene. Therefore, it seems tempting to speculate that the CNVR_3425.1 first damps FENDRR expression and then indirectly results in a net decrease of FOXF1. In addition, there remains no doubt that FENDRR would cause altered expressions of a series of genes with respect to its epigenetic regulation ability (43) and its effect on FOXF1, which is a probable transcription activator for a number of lung-specific genes (47). Indeed, our data showed that overexpression of FENDRR results in altered expression of abundant genes. Also, TCGA data further revealed an abundant of coding genes to be associated with FENDRR. These findings further supported FENDRR to be involved in lung cancer and COPD development. Thus, upregulation of FENDRR may be exploited in the treatment of the two diseases. It is unlikely that the current finding CNVR_3425.1 confers to increased disease risk was achieved by chance, considering that the association was consistent in two studied populations and two contextual diseases and had strong functional experimental evidences. We have also achieved >99.0% study power for both lung cancer and COPD case-control studies. However, being hospital-based case-control studies, bias such as information or selection bias was inevitable. This might cause an error in estimation on association strength between the gCNV and the risk of lung cancer as well as COPD. Moreover, the functional assays only presented an indirect connection between the CNVR_3425.1 and lung cancer phenotypes pass by FENDRR. We have tried to construct a stable A549 cell line with defined number of integrates of the promoter region that are expected to carry 4-copy or 3-copy using the Crispr/cas9 technology, and thus, we can directly observe the genetic effect of the CNVR_3425.1 on FENDRR expression and cell phenotypes. Unfortunately, we always failed to conduct such an experiment due to technical restriction. Based on case-control studies and a series of functional assays, we have identified the CNVR_3425.1 to be susceptible loci for lung cancer and COPD in Chinese. The ≥3-copy predispose carriers to develop lung cancer and COPD by weakening transcriptional efficiency of the FENDRR promoter and further downregulates FENDRR expression. FENDRR functions to inhibit lung cancer development by affecting expression of an abundant number of genes, including an upregulation of tumor suppressor FOXF1. These data supported the CNVR_3425.1 to be an idiotypic predictor for the risk of lung cancer and COPD in Chinese, and targeted molecular therapy owing to FENDRR upregulation may be valuable pathway to fight two diseases. Supplementary material Supplementary data are available at Carcinogenesis online. Funding This study was supported by the National Natural Scientific Foundation of China grants 81473040, 81673267 (J.Lu) and by 81402753, 81672303 (L.Y). Guangdong Provincial Major Projects Grant 2014KZDXM046 (J.Lu), Guangzhou Science and Technology Program Pearl River Nova projects Grant 201710010049 (L.Y.), Guangzhou Education Bureau Major projects Grant 1201610122 (L.Y.) and Guangdong education Department Characteristic innovation project Grants 1201541589 and 2015KTSCX116 (L.Y.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We thank Dr. Soham Datta for his assistance on English revision. We thank Dr. Yuan Guo, Dr. Lin Liu, Dr. Dongsheng Huang and Dr. Yumin Zhou for their assistance on subjects’ recruitment. Conflict of Interest Statement The authors declare that they have no competing interests. Authors’ contributions J.L. and L.Y. conceived and designed the experiments; L.Y. analyzed the data and wrote the paper; D.W. and J.C. performed major functional experiments; J.C., Y.L. and L.L. performed genotyping assays; Y.C. and F.Q. performed subsidiary functional experiments; B.Y. contributed reagents/materials/analysis tools; Y.Z. help revise the paper. 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CarcinogenesisOxford University Press

Published: Mar 1, 2018

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