Abstract Gliomas are characterized by a malignant phenotype with proliferation, cell cycle arrest and invasion. To explore the biological consequences of epigenetically regulated miRNAs, we performed a microarray-based screening (whose expression was affected by 5-AZA treatment) followed by bisulfite sequencing validation. We found that miR-134 as an epigenetically regulated suppressor gene with prognostic value in gliomas. MicroRNA-134 was downregulated in high-grade gliomas, especially in GBM samples. Functional studies in vitro and in vivo in mouse models showed that overexpression of miR-134 was sufficient to reduce cell cycle arrest, cell proliferation and invasion. Target analysis and functional assays correlated the malignant phenotype with miR-134 target gene KRAS, an established upstream regulator of ERK and AKT pathways. Overall, our results highlighted a role for miR-134 in explaining the malignant phenotype of gliomas and suggested its relevance as a target to develop for early diagnostics and therapy. Introduction Glioblastomas is a deadly central nervous system tumor that is characterized by a complex genomic landscape and profound heterogeneity (1). Identification of the underlying pathogenic mechanisms involved in the initiation and progression of this tumor is critical for developing more effective treatments. Cancer-specific DNA methylation changes are hallmarks of human cancers, in which global DNA hypomethylation is often seen concomitantly with hypermethylation of CpG islands (2). Promoter CpG island hypermethylation generally results in transcriptional silencing of the associated gene. MicroRNAs (miRNAs) are small non-coding RNAs which act as post-transcriptional regulators of gene expression. Through regulation of specific target genes, a wide variety of biologic processes including cellular differentiation, proliferation and apoptosis are regulated by miRNAs (3,4). Thus, deregulated expression of certain miRNAs may lead to alterations of these processes and to the development of a malignant phenotype. Beside chromosomal loss and alterations of the miRNA processing machinery, methylation has been identified as a mechanism which may cause downregulation of miRNA gene expression in cancer cells (5,6). Previously, miR-34a, miR-192, miR-196b and miR-892b have been reported as examples for methylated miRNA-encoding genes (referred to as methylated miRNAs) in lung adenocarcinomas and other malignancies (7–9). However, knowledge about methylation-mediated miRNA silencing in glioma is still very limited to date. To gain more information about the role of methylation in miRNA silencing, we conducted a microarray-based screening for methylation regulated miRNAs. By further validation in glioma cell lines, primary tumors, normal brain tissue, in vitro and in vivo assays, we found that miR-134 was epigenetically silenced and could mitigate a malignant phenotype in gliomas. Materials and methods Cell culture and 5-AZA treatment Human glioma cell lines U87, LN229, U251, SHG44, U373 and H4 were cultured in DMEM medium (Hyclone) supplemented with 10 % fetal bovine serum (Hyclone). Human astrocyte cell lines (HA) (ScienCell, Carlsbad) were cultured in AM medium (ScienCell, Carlsbad), including 500 ml of basal medium, 10 ml of fetal bovine serum and 5 ml of astrocyte growth supplement. All cells were incubated at 37°C in an atmosphere of 5% CO2. To demethylate the epigenetically silenced miRNAs, these cell lines were cultured with 0.5 μM of 5-AZA (Sigma-Aldrich, St. Louis, MO) for 5 days. Fresh 5-AZA was replaced every 24 h. Cells treated with 5-AZA on days 0 and 5 were harvested for miRNA expression quantification. MiRNA expression data from glioma tissue samples The normalized miRNA expression data and the corresponding clinical information from the Cancer Genome Atlas (TCGA, normal brain control: N = 10; GBM samples: N = 505, http://cancergenome.nih.gov/) and Chinese Glioma Genome Atlas (CGGA, low-grade glioma (LGG): N = 63; high-grade glioma (HGG): N = 135, http://www.cgga.org.cn) were downloaded for the analysis of expression patterns of miR-134 in normal brain controls, LGG and HGG samples. Patients and samples Human glioma tissue samples (LGG, N = 22; HGG, N = 43) and normal brain controls (patients undergoing decompressive craniectomy for traumatic brain injury, N = 10) were obtained from Beijing Tiantan Hospital for validation of the methylation and expression status of miR-134. The tissues were collected within 30 min after resection. All tissue samples were immediately stored in liquid nitrogen after resection. This study was approved by the Ethics Committee of Beijing Tiantan Hospital, and written informed consent was obtained from all participants. All samples were histologically classified and graded according to WHO guidelines by two clinical neuropathologists. RNA extraction and miRNA expression quantification Total RNAs were extracted from patient tissue specimens and cell lines using TRIzol reagent (Invitrogen). Only RNA samples with RIN ≥7.0 were used for further analysis. The miRNA expression microarray (Agilent human miRNA(8*60k) v18.0) was used for identification of epigenetically silenced miRNAs in three glioma cell lines (U251, U87, H4; before and after 5-AZA treatment). Quantitative reverse transcriptase-polymerase chain reaction (qRT-PCR) analysis was used to validate the expression pattern of miR-134 in HA, 6 glioma cell lines, 10 normal brain controls and 65 glioma tissues in the validation cohort. The experiments were performed using a standard SYBR PrimeScript miRNA RT-PCR kit (Takara, Japan) in a C1000™ Thermal Cycler and a CFX96™ Real-Time system (Bio-Rad) according to the manufacturer’s protocol. The relative expression value of miRNA was calculated using comparative method after normalization against U6 rRNA (The 5ʹ–3ʹ primer of human miR-134: 5ʹ-TGTGACTGGT TGACCAGA GGG-3ʹ). DNA extraction and bisulfite sequencing Genomic DNA was extracted from frozen tumor tissues using the QIAamp DNA Mini Kit (Qiagen) according to the manufacturer’s protocol. DNA concentration and quality were measured using the Nano-Drop ND-2000 spectrophotometer (NanoDrop Technologies, Houston, TX). Bisulfite sequencing spanning the promoter regions of miR-134 was used for the validation of methylation status of glioma samples in the validation cohort (N = 65), Of the 14 CpG sites we tested, a sample would be considered as hypermethylated if the average methylation rate >10%, and vice versa. MiRNA target prediction Candidate targets of miR-134 were predicted by TargetScan v6.2 (http://www.Targetscan.org/), PicTar (http://www.PicTar.org/) and RNA22 v2 (https://cm.jefferson.edu/rna22/). The overlapped target genes were used for further analysis. Luciferase assay For the luciferase reporter assay, the Luci-KRAS (containing the predicted matching regions of miR-134) and negative control (mutant region) were designed and synthesized by GenePharma Inc. (Shanghai, China). Firstly, U87 cell were transfected with an KRAS 3ʹUTR luciferase plasmid followed by transfection with miR-134 mimic or control miRNA in 12-well plates and then were collected and lysed for luciferase assay 48 h after transfection. The luciferase kit (Promega, Madison, WI) and Luciferase Reporter Assay System were used to detect the luciferase activities according to the manufacturer’s protocols. The Renilla luciferase was used as normalization. MiRNA oligo and cell transfection Hsa-miR-134 and miR-NC (normal control) oligo were chemically synthesized by GenePharma Inc. (Shanghai, China) and transfected using the riboFECT™ CP reagent (RIBBIO, Guangzhou, China) according to the manufacturer’s instructions. The further experiments would be performed within 72 h after transfection. Lentiviruses and cell infection Lentiviruses [miR-134 expression lentiviruses and its negative control containing the green fluorescent protein (GFP)] were packaged by GenePharma Inc. (Shanghai, China). Totally, 8 × 104 U87 and LN229 glioma cells were seeded in six-well plates and infected with the miR-134 and negative control lentiviruses. The expressions of the GFP were detected through the fluorescence microscopy. The further experiments would be performed when there were 80% cells expressing the GFP. Western blotting and immunohistochemistry Western blot and immunohistochemistry assays were performed as previously described (10). Primary antibodies including rabbit anti-human p-ERK1/2, p-AKT, KRAS (Cell Signaling Technology), rabbit anti-human ERK, AKT (Beyotime, Jiangsu, China), mouse anti-human GAPDH (Proteintech, Chicago) and secondary antibody were HRP-conjugated anti-rabbit or anti-mouse IgG (ORIGENE, Beijing, China). Equal protein loading was assessed by the expression of GAPDH. The Western blots were visualized by chemiluminescence (ECL) reagents (Thermo) and Bio-Rad ChemiDoc XRS+ system (Bio-Rad). immunohistochemistry was visualized using light microscope. Cell cycle analysis Cells were washed with cold PBS, collected with EDTA-free trypsin and fixed with 70% ethanol in −20% overnight. DNA staining was performed by using propidium iodide (50 µg/ml) after the cells incubated with RNase A (1 mg/ml) in 37°C for 30 min. The DNA content of the cells was analyzed by an Accuri c6 flow cytometer (BD Biosciences). Apoptosis assays Annexin V-FITC/PI kit (BD PharMingen, San Diego, CA) was used for the detection of cell apoptosis according to the manufacturer’s instructions. Briefly, U87 and LN229 cells were trypsinized and 5 × 105 cells were washed with PBS. Cells were then processed for labeling with Annexin V/FITC and propidium iodide (PI), and analyzed by flow cytometry. Colony formation assay Colony formation assay was used to test the capacity of cell colony growth. The U87 and LN229 glioma cells were collected after transfection, and 200 cells were seeded into the six-well plates. After 2 weeks, the colonies were fixed with methanol for 10 min and stained with 1% crystal violet (Sigma) for 1 min. The number of colonies were measured. Cell growth assays The MTT assay was used to measure relative cell growth. U87 and LN229 cells were plated at 2 × 103 cells per well in 96-well plates with five replicate wells for each condition, transfected with miR-134 mimics and miR-NC mimics and measured once every 24 h from post-transfection. Twenty microliters of 5 mg/ml MTT (Sigma) was added to each well; then, the 96-well plates were incubated for 4 h. After adding 150 μl DMSO to each well, the 96-well plate was shaken on the Horizontal shaker for 15 min. The 490 nm absorbance was measured to assess the cell viability using a microplate reader. Migration and invasion assays Transwell chamber assay was used to evaluate the cell migration and invasion according to the manufacturer’s instructions. For invasion assay, 3 × 104 transfected cells were seeded on the transwell insert coated (BD) with extracellular matrix, while cell migration assay did coat (Costar, NY) without extracellular matrix. After the cells were incubated at 37°C for 24 h, cells which were adherent to the upper surface of the filter were removed and stained with crystal violet (Sigma). The number of cells was counted. Orthotopic nude mouse models and treatment Nude mice (BALB/c-A, 4 weeks old) were purchased from the Animal Center at the Cancer Institute of the Chinese Academy of Medical Science and maintained in specific pathogen-free conditions. A nude mouse tumor xenograft model was established as previously described (11). Twenty model mice were divided into miR-134 group and miR-NC group(n = 10/group) To establish intracranial gliomas, after transfection with luciferase lentivirus, U87 glioblastoma cells stably expressing miR-134 or miR-NC were injected stereotactically into brain of each nude mouse (0.5 × 105 cells). The mice underwent bioluminescence imaging every 10 days for 40 days. Additionally, the overall survival time of the two groups was monitored. The mice bearing xenograft tumors were euthanized after 40 days. The tumor tissues were removed and fixed in formalin, and paraffin embedded sections were prepared for immunohistochemical analysis. Statistical analysis R (12) and GraphPad Prism 6 (GraphPad Software Inc., La Jolla, CA) were used for statistical analyses. Differences in clinical and molecular features were evaluated with the Student’s t or Chi-square tests. The Student’s t test was also used to differential expression analysis. The overall survival analysis was performed by Kaplan–Meier method with log-rank test. Statistical significance was defined as a two-tailed P value < 0.05. Results Identification of methylation regulated miRNAs in three glioma cell lines. In order to find out the methylation silenced miRNAs that might be potential tumor suppressors in glioma pathogenesis, we treated three human glioma cell lines (U87, U251 and H4) with 5-aza-2-deoxycytidine (5-AZA). We used miRNA expression microarray to measure miRNA expression levels in each cell line. RNA was extracted on day 0 and day 5 after treatment with the DNA demethylating drug. After overlapping the differentially expressed miRNAs, only miR-134 was observed in significant commonly upregulated group (Figure 1A; Supplementary Figures 1–3, Supplementary Table 1, available at Carcinogenesis Online). And no miRNA was in the commonly down-regulated group (Figure 1B; Supplementary Figures 1–3, available at Carcinogenesis Online). We also validated miR-134 expression levels by quantitative RT-PCR (qRT-PCR) before and after treatment with 5-AZA in six glioma cell lines (U251, U373, SHG44, U87, LN229 and H4). This microRNA showed significant upregulation after 5-AZA treatment in all these cell lines (Figure 1C). CpG island, which is considered as key regulators in promoter methylation, is characterized by a high frequency of CG sequence in DNA. We also analyzed the DNA sequence of miR-134 promoter region (5 kb upstream), and found several CpG island regions (Figure 1D), which demonstrated the possibility of epigenetic regulation of miR-134 in gliomas. Figure 1. View largeDownload slide MicroRNA-134 was epigenetically silenced in glioma cell lines. (A) The overlap of upregulated miRNAs after 5-AZA treatment in three cell lines. (B) The overlap of down-regulated miRNAs after 5-AZA treatment in three cell lines. (C) qRT-PCR validation of miR-134 expression in six cell lines. (D) CpG island analysis in the promoter region of miR-134. Figure 1. View largeDownload slide MicroRNA-134 was epigenetically silenced in glioma cell lines. (A) The overlap of upregulated miRNAs after 5-AZA treatment in three cell lines. (B) The overlap of down-regulated miRNAs after 5-AZA treatment in three cell lines. (C) qRT-PCR validation of miR-134 expression in six cell lines. (D) CpG island analysis in the promoter region of miR-134. MiR-134 is epigenetically downregulated in glioma tissues and correlates with improved prognosis The expression level of miR-134 was assessed in two independent glioma datasets. The TCGA miRNA expression microarray data were derived from 10 normal brain samples and 505 GBM samples. The microarray data of CGGA contained 63 low-grade gliomas and 135 high-grade gliomas. There was a higher expression of miR-134 in GBM and high-grade glioma samples as compared to normal brain tissues and low-grade gliomas (NBT versus GBM, P = 0.0002, Figure 2A; LGG versus HGG, P = 0.0001, Figure 2B). To further validate these results, the expression level of miR-134 in normal brain specimens, LGGs and high-grade gliomas were examined by using quantitative real-time PCR (qRT-PCR), and a similar expression pattern was shown (Normal versus LGG at P < 0.0001; LGG versus HGG at P < 0.0001, Figure 2C). Figure 2. View largeDownload slide MicroRNA-134 was epigenetically silenced in high-grade glioma and GBM samples and conferred a favorable prognosis. (A) miR-134 showed a lower expression in GBM samples. (B) miR-134 showed a lower expression in high grade gliomas. (C) The expression pattern of miR-134 was validated in an independent cohort. (D) There was a higher proportion of HGG samples with miR-134 hypermethylation. (E) There was a higher proportion of hypomethylated samples with miR-134 high expression. Patients with higher expression of miR-134 showed more favorable prognosis. (F, 198 grades II–IV glioma patients; G, 82 primary GBM patients; H, 43 high grade glioma patients). Figure 2. View largeDownload slide MicroRNA-134 was epigenetically silenced in high-grade glioma and GBM samples and conferred a favorable prognosis. (A) miR-134 showed a lower expression in GBM samples. (B) miR-134 showed a lower expression in high grade gliomas. (C) The expression pattern of miR-134 was validated in an independent cohort. (D) There was a higher proportion of HGG samples with miR-134 hypermethylation. (E) There was a higher proportion of hypomethylated samples with miR-134 high expression. Patients with higher expression of miR-134 showed more favorable prognosis. (F, 198 grades II–IV glioma patients; G, 82 primary GBM patients; H, 43 high grade glioma patients). The promoter methylation status of these samples was also analyzed by bisulfite sequencing. 63.64% (14/22) of LGG samples were hypomethylated while 65.12% (28/43) HGG samples were hypermethylated (P = 0.0273, Chi-square test, Figure 2D). In the HGG samples, the miR-134 expression of 71.43% (15/21) samples in hypermethylated group were lower than median and 72.73% (16/22) samples in hypomethylated group were higher than median (P = 0.0038, Chi-square test, Figure 2E). In addition, we also evaluated the prognostic value of miR-134 in 198 glioma patients (Figure 2F) and 82 primary GBM (pGBM, Figure 2G) patients from CGGA dataset. The results showed that patients with high expression of miR-134 had significantly longer survival (P = 0.0132 and P = 0.0307; Figure 2F and G). We then validated the prognostic value of miR-134 expression in HGG samples from validation cohort (N = 43). We observed a similar pattern. Then, miR-134 expression and clinical characteristics, including age, gender, grade and IDH status, were tested for their relationship with overall survival using a univariate Cox proportional hazards model. The result indicated that miR-134 expression, age, grade and IDH status were associated with overall survival. After that, we performed a multivariate Cox proportional hazards analysis incorporating miR-134 expression, age, grade and IDH status. The analysis found that all features were independent (Table 1). Those findings indicated that miR-134 was an independent positive prognostic factor in gliomas. Table 1. Cox hazard regression analyses of clinicopathologic factors and the miR-134 for overall survival in CGGA dataset (N = 196) Univariate cox model Multivariate cox model Hazard ratio 95% CI P value Hazard ratio 95% CI P value Male versus female 1.425 0.989–2.055 0.0577 WHO grade IV versus II/III 4.658 3.192–6.797 1.44e−15 3.565 2.291–5.549 2.6e-8 Age ≥60 versus <60 2.176 1.380–3.433 8.27e−4 1.812 1.086–3.038 0.022 IDH status MUT versus WT 0.328 0.220–0.490 5.28e−08 0.571 0.365–0.897 0.015 MiR-134 high versus low 0.538 0.366–0.791 1.61e−3 0.620 0.416–0.934 0.023 Univariate cox model Multivariate cox model Hazard ratio 95% CI P value Hazard ratio 95% CI P value Male versus female 1.425 0.989–2.055 0.0577 WHO grade IV versus II/III 4.658 3.192–6.797 1.44e−15 3.565 2.291–5.549 2.6e-8 Age ≥60 versus <60 2.176 1.380–3.433 8.27e−4 1.812 1.086–3.038 0.022 IDH status MUT versus WT 0.328 0.220–0.490 5.28e−08 0.571 0.365–0.897 0.015 MiR-134 high versus low 0.538 0.366–0.791 1.61e−3 0.620 0.416–0.934 0.023 View Large KRAS is identified to be a target of miR-134 We used three publicly available databases to predict miR-134 targets: TargetScan v6.2 (http://www.Targetscan.org/), PicTar (http://www.PicTar.org/) and RNA22 v2 (https://cm.jefferson.edu/rna22/). The results indicated that eight overlapping genes (KRAS, PSD3, HIC2, PHF8, GNAO1, TCF21, STXBP1, C1orf21, Figure 3A, Supplementary Table 2, available at Carcinogenesis Online) were potential targets of miR-134 with high possibility. Kirsten rat sarcoma viral oncogene (KRAS) which is involved in multiple oncogenic pathways has been reported in various malignancies (13–15), including cervical cancer, mucinous adenoma, ductal carcinoma of the pancreas and colorectal carcinoma. To validate the miRNA target interaction, luciferase reporter system with a wild-type (KRAS-WT) and mutant (KRAS-MUT) sequence of KRAS 3ʹ-UTR was used (Figure 3B). The luciferase activity was significantly lower in miR-134 than miR-NC transfected cells in KRAS-WT group but this pattern was not encountered in cells transfected with miR-134 and miR-NC in KRAS-MUT group (Figure 3C). Therefore, our results showed that miR-134 directly targeted KRAS through binding its 3′-UTR region. Figure 3. View largeDownload slide KRAS was a target of miR-134. (A) Eight genes remained as the overlap of three miRNA target prediction databases. (B) The structure of vectors for miR-134-KRAS interaction validation in luciferase assay. (C) The luciferase activity significantly decreased after co-transfection of wild type KRAS 3ʹ-UTR and miR-134. (D) miR-134 could inhibit the expression of KRAS and activation of downstream ERK and AKT pathways. (E) The inhibition could be rescued by transfection of KRAS. Figure 3. View largeDownload slide KRAS was a target of miR-134. (A) Eight genes remained as the overlap of three miRNA target prediction databases. (B) The structure of vectors for miR-134-KRAS interaction validation in luciferase assay. (C) The luciferase activity significantly decreased after co-transfection of wild type KRAS 3ʹ-UTR and miR-134. (D) miR-134 could inhibit the expression of KRAS and activation of downstream ERK and AKT pathways. (E) The inhibition could be rescued by transfection of KRAS. The downstream signaling pathways of KRAS were also examined by western blot after transfection with miR-NC or miR-134. Our experiments indicated that over-expression of miR-134 strongly decreased the phosphorylation AKT (pAKT), the phosphorylation ERK (pERK) and the expression of KRAS compared to the miR-NC group (Figure 3D). Then we co-transfected miR-134 with or without KRAS lentivirus. The result demonstrated that KRAS over-expression rescued the activation level of ERK and AKT (Figure 3E). Therefore, our experiments demonstrated that KRAS was a direct target of miR-134 and miR-134 could suppress the activation of downstream pathway of KRAS. MicroRNA-134 could suppress the malignant phenotype of glioma cell lines To further investigate the biological functions of miR-134 in glioma cells, U87 and LN229 glioma cells were transfected with miR-134 and miR-NC mimics. By using qRT-PCR, the miR-134 expression was significantly increased (Figure 4A) and the KRAS expression was significantly decreased (Figure 4B) in U87 and LN229 cells compared with their control groups. There was a lower proportion of cells in S/G2M phase and a higher the proportion of cells in G1/G0 phase with overexpression of miR-134 (Figure 4C and D). In colony formation assay, miR-134 transfection could suppress the formation of colonies while this phenomenon could be rescued by KRAS co-transfection (Figure 4E). In MTT assays, miR-134 overexpression significantly increased the proliferation of cells (Figure 4F). Figure 4. View largeDownload slide MicroRNA-134 could inhibit a malignant phenotype of glioma cell lines and KRAS could rescue the inhibition. (A) After transfection, miR-134 was significantly upregulated. (B) KRAS expression level was significantly downregulated after miR-134 transfection. (C) There was a lower proportion of cells in S/G2M phase after miR-134 transfection. (D) There was a higher proportion of apoptotic cells after miR-134 transfection. (E) miR-134 could inhibit the formation of colonies and KRAS could rescue this inhibition. (F) miR-134 could inhibit the proliferation of glioma cells. (G) miR-134 could inhibit the migration of glioma cells, which could be rescued by KRAS transfection. (H) miR-134 could inhibit the invasion of glioma cells, which could be rescued by KRAS transfection. Figure 4. View largeDownload slide MicroRNA-134 could inhibit a malignant phenotype of glioma cell lines and KRAS could rescue the inhibition. (A) After transfection, miR-134 was significantly upregulated. (B) KRAS expression level was significantly downregulated after miR-134 transfection. (C) There was a lower proportion of cells in S/G2M phase after miR-134 transfection. (D) There was a higher proportion of apoptotic cells after miR-134 transfection. (E) miR-134 could inhibit the formation of colonies and KRAS could rescue this inhibition. (F) miR-134 could inhibit the proliferation of glioma cells. (G) miR-134 could inhibit the migration of glioma cells, which could be rescued by KRAS transfection. (H) miR-134 could inhibit the invasion of glioma cells, which could be rescued by KRAS transfection. To further detect the biological roles of miR-134 in gliomas, we used the transwell assay to evaluate the migratory and invasive behavior of U87 and LN229 cells. The results revealed that overexpression miR-134 triggered the suppression of the migratory and invasive ability in vitro which could be further rescued by KRAS co-transfection (Figure 4G and H). MiR-134 could inhibit glioma growth in vivo To further investigate the effect of miR-134 in vivo, we established the GBM orthotopic nude mouse model use U87 cell lines as described previously. U87 glioblastoma cells transfected and stably expressing luciferase and miR-134 or miR-NC were injected stereotactically into brains of nude mouse. 5 × 103 U87 cells transfected with miR-NC or miR-134 were injected, and the tumor volume was examined over time (10, 20 and 30 days) (Figure 5A). MiR-134 group resulted in a significant reduction of the intracranial tumor volume compared with the miR-NC group. Figure 5. View largeDownload slide MicroRNA-134 could inhibit tumor growth in mouse models. (A) miR-134 transfected gliomas showed a slower growth rate in mouse models. (B) Immunohistochemistry results showed miR-134 transfection could inhibit the expression of KRAS and activation of ERK and AKT pathways. (C) Mice injected with miR-134 transfected glioma cells showed a better survival. Figure 5. View largeDownload slide MicroRNA-134 could inhibit tumor growth in mouse models. (A) miR-134 transfected gliomas showed a slower growth rate in mouse models. (B) Immunohistochemistry results showed miR-134 transfection could inhibit the expression of KRAS and activation of ERK and AKT pathways. (C) Mice injected with miR-134 transfected glioma cells showed a better survival. Through the immunohistochemical analysis, miR-134 overexpression could significantly suppress the expression of KRAS and the activation of ERK and AKT pathway (Figure 5B). Furthermore, the overall survival time of miR-134 group was longer than miR-NC group (P = 0.0061, Figure 5C). These results demonstrated that miR-134 could inhibit tumorigenesis and prolong the overall survival time in vivo. Discussion MicroRNAs play important roles by interaction with specific region of mRNA in many cellular biological functions, such as proliferation, differentiation, apoptosis and development (3,16). It has been proved that miRNA may act as tumor suppressors or proto-oncogenes during tumorigenesis in many human cancers (17,18). Accumulated evidences indicated that miRNA might be candidates as biomarkers for diagnosis and treatment of gliomas (19,20). MicroRNA-134, located at 14q32 (21) was first identified as a brain‑specific miRNA, localized in the synaptic-dendritic compartment of hippocampal neurons and involved in the regulation of the neuronal microstructure (22,23). Recent studies have demonstrated that miR-134 can inhibit proliferation, migration, invasion in lung cancer and hepatocellular carcinoma (24,25). Apart from these, miR-134, downregulated in glioma (26), could discriminate oligodendrogliomas from glioblastoma (27) and inhibit glioma progression by targeting receptor tyrosine kinases (RTKs) (28), KRAS and STAT3 (29–32). Meanwhile, Semliki Forest virus (SFV) which was inserted target sequences complementary to miR-134 (SFV4miRT) may become an excellent candidate for treatment of glioma (23). Some researches demonstrated that miR-134 boosted expression in hypoxic environment in glioma-initiating cells (33). However, the molecular mechanisms of miR-134 expression regulation and the downstream signaling pathways in glioma still need to be elucidated. Accumulating evidence suggests that DNA methylation could regulate miRNA expression during tumor progression (8,34,35). In this study, by integrating high through-put data, validation data from glioma tissue samples, in vitro and in vivo assays, we proved that miR-134 could be epigenetically silenced in gliomas and could inhibit the malignant phenotypes by targeting KRAS. We verified that overexpression miR-134 could suppress cell proliferation and induce cell apoptosis due to an arrest in G0/G1 phase of the cell cycle and patients with higher miR-134 expression had significantly longer overall survival. Then we explored the role of KRAS in miR-134-mediated tumor suppression. KRAS is one of the RAS family genes which encodes a membrane-associated 21 kDa GTP-binding protein. By switching from the GTP-bound active form to the GDP-bound inactive form, KRAS protein plays important roles in turning on or off their downstream effectors. Notably, the expression level of KRAS is positively correlated to the activity of ERK pathway and its downstream regulators [phosphorylation level of ERK (pERK), phosphorylation level of AKT (pAKT)] (36–38). Excessive activation of ERK pathway which is necessary for cell growth and AKT pathway which is essential for cell survival is significantly associated with the aggressiveness of tumors (39,40). Although the mutation frequency of KRAS is relatively low [0.3% in grades II–III gliomas (41)], it has been reported to play an active role in the progression of gliomas (42). But the potential mechanisms are still unknown. Epigenetic or post-transcriptional regulation might be vital mechanisms. KRAS has been demonstrated as a target gene of miR-134 in other malignancies (43–45). Our study further analyzed the relationship between miR-134 and KRAS by in vitro and in vivo assays. Our experiments indicated that the induction of miR-134 could inhibit the malignant phenotype including proliferation, cell cycle, migration and invasion by targeting KRAS. Conclusion In summary, our study identified miR-134 which was epigenetically silenced in gliomas and could inhibit the malignant phenotype of gliomas from three glioma cell lines. We validated that miR-134 could inhibit ERK and AKT pathway through targeting KRAS, highlighting the potential of miR-134 as a candidate therapeutic target in glioma patients. Supplementary material Supplementary material can be found at Carcinogenesis online. Funding This work has been supported by grants from the National Nature Science Foundation of China (81502494, 81402052, 81502495, 81702460, 81502606); Natural Science Foundation of Heilongjiang Province (H2016026); China Postdoctoral Science Foundation Special Funding (2017T100252) and (2016M590293); Postdoctoral Fund of Heilongjiang Province (LRB14-422), the National Key Research and Development Plan (No. 2016YFC0902500); Capital Medical Development Research Fund (2016-1-1072). Conflict of Interest Statement: None declared. Abbreviations 5-AZA 5-aza-2-deoxycytidine CGGA Chinese Glioma Genome Atlas KRAS Kirsten rat sarcoma viral oncogene GFP green fluorescent protein HGG high-grade glioma LGG low-grade glioma miRNA microRNA qRT-PCR quantitative reverse transcriptase-polymerase chain reaction References 1. Wang, Y.et al. ( 2013) Understanding high grade glioma: molecular mechanism, therapy and comprehensive management. Cancer Lett ., 331, 139– 146. Google Scholar CrossRef Search ADS PubMed 2. Noushmehr, H.et al. ; Cancer Genome Atlas Research Network. ( 2010) Identification of a CpG island methylator phenotype that defines a distinct subgroup of glioma. Cancer Cell , 17, 510– 522. Google Scholar CrossRef Search ADS PubMed 3. Bartel, D.P. ( 2004) MicroRNAs: genomics, biogenesis, mechanism, and function. Cell , 116, 281– 297. Google Scholar CrossRef Search ADS PubMed 4. Guo, M.et al. ( 2014) miR-656 inhibits glioma tumorigenesis through repression of BMPR1A. Carcinogenesis , 35, 1698– 1706. Google Scholar CrossRef Search ADS PubMed 5. Samuel, N.et al. ( 2016) Genome-wide DNA methylation analysis reveals epigenetic dysregulation of microRNA-34A in TP53-associated cancer susceptibility. J Clin Oncol , 34, 3697– 3704. Google Scholar CrossRef Search ADS 6. Wong, K.Y.et al. ( 2012) DNA methylation of microRNA genes in multiple myeloma. Carcinogenesis , 33, 1629– 1638. Google Scholar CrossRef Search ADS PubMed 7. Tellez, C.S.et al. ( 2016) miR-196b is epigenetically silenced during the premalignant stage of lung carcinogenesis. Cancer Res ., 76, 4741– 4751. Google Scholar CrossRef Search ADS PubMed 8. Botla, S.K.et al. ( 2016) Early epigenetic downregulation of microRNA-192 expression promotes pancreatic cancer progression. Cancer Res ., 76, 4149– 4159. Google Scholar CrossRef Search ADS PubMed 9. Jiang, L.et al. ( 2016) miR-892b silencing activates NF-κB and promotes aggressiveness in breast cancer. Cancer Res ., 76, 1101– 1111. Google Scholar CrossRef Search ADS PubMed 10. Wang, H.et al. ( 2015) Upregulation of miR-181s reverses mesenchymal transition by targeting KPNA4 in glioblastoma. Sci. Rep ., 5, 13072. Google Scholar CrossRef Search ADS PubMed 11. Yang, F.et al. ( 2017) miR-181d/MALT1 regulatory axis attenuates mesenchymal phenotype through NF-κB pathways in glioblastoma. Cancer Lett ., 396, 1– 9. Google Scholar CrossRef Search ADS PubMed 12. R Core Team( 2017) R: A language and environment for statistical computing . R Foundation for Statistical Computing, Vienna, Austria. 13. Cancer Genome Atlas Research Network. ( 2017) Integrated genomic characterization of pancreatic ductal adenocarcinoma. Cancer Cell , 32, 185– 203 e13. CrossRef Search ADS PubMed 14. Cherniack, A.D.et al. ; Cancer Genome Atlas Research Network. ( 2017) Integrated molecular characterization of uterine carcinosarcoma. Cancer Cell , 31, 411– 423. Google Scholar CrossRef Search ADS PubMed 15. Cancer Genome Atlas Research Network.et al. ( 2017) Integrated genomic and molecular characterization of cervical cancer. Nature , 543, 378– 384. CrossRef Search ADS PubMed 16. He, L.et al. ( 2004) MicroRNAs: small RNAs with a big role in gene regulation. Nat. Rev. Genet ., 5, 522– 531. Google Scholar CrossRef Search ADS PubMed 17. Volinia, S.et al. ( 2006) A microRNA expression signature of human solid tumors defines cancer gene targets. Proc. Natl. Acad. Sci. USA , 103, 2257– 2261. Google Scholar CrossRef Search ADS 18. Lu, J.et al. ( 2005) MicroRNA expression profiles classify human cancers. Nature , 435, 834– 838. Google Scholar CrossRef Search ADS PubMed 19. Guo, T.et al. ( 2014) MicroRNA-320a suppresses in GBM patients and modulates glioma cell functions by targeting IGF-1R. Tumour Biol ., 35, 11269– 11275. Google Scholar CrossRef Search ADS PubMed 20. Han, L.et al. ( 2012) MicroRNA-21 expression is regulated by β-catenin/STAT3 pathway and promotes glioma cell invasion by direct targeting RECK. CNS Neurosci. Ther ., 18, 573– 583. Google Scholar CrossRef Search ADS PubMed 21. Schratt, G.M.et al. ( 2006) A brain-specific microRNA regulates dendritic spine development. Nature , 439, 283– 289. Google Scholar CrossRef Search ADS PubMed 22. Yin, C.et al. ( 2013) Hepatocyte nuclear factor-4α reverses malignancy of hepatocellular carcinoma through regulating miR-134 in the DLK1-DIO3 region. Hepatology , 58, 1964– 1976. Google Scholar CrossRef Search ADS PubMed 23. Ramachandran, M.et al. ( 2017) Safe and effective treatment of experimental neuroblastoma and glioblastoma using systemically delivered triple microRNA-detargeted oncolytic semliki forest virus. Clin. Cancer Res ., 23, 1519– 1530. Google Scholar CrossRef Search ADS PubMed 24. Qin, Q.et al. ( 2016) MiR-134 inhibits non-small cell lung cancer growth by targeting the epidermal growth factor receptor. J. Cell. Mol. Med ., 20, 1974– 1983. Google Scholar CrossRef Search ADS PubMed 25. Zha, R.et al. ( 2014) Genome-wide screening identified that miR-134 acts as a metastasis suppressor by targeting integrin β1 in hepatocellular carcinoma. PLoS One , 9, e87665. Google Scholar CrossRef Search ADS PubMed 26. Niu, C.S.et al. ( 2013) MiR-134 regulates the proliferation and invasion of glioblastoma cells by reducing Nanog expression. Int. J. Oncol ., 42, 1533– 1540. Google Scholar CrossRef Search ADS PubMed 27. Lages, E.et al. ( 2011) MicroRNA and target protein patterns reveal physiopathological features of glioma subtypes. PLoS One , 6, e20600. Google Scholar CrossRef Search ADS PubMed 28. Szerlip, N.J.et al. ( 2012) Intratumoral heterogeneity of receptor tyrosine kinases EGFR and PDGFRA amplification in glioblastoma defines subpopulations with distinct growth factor response. Proc. Natl. Acad. Sci. USA , 109, 3041– 3046. Google Scholar CrossRef Search ADS 29. Sherry, M.M.et al. ( 2009) STAT3 is required for proliferation and maintenance of multipotency in glioblastoma stem cells. Stem Cells , 27, 2383– 2392. Google Scholar CrossRef Search ADS PubMed 30. Normanno, N.et al. ( 2009) Implications for KRAS status and EGFR-targeted therapies in metastatic CRC. Nat. Rev. Clin. Oncol ., 6, 519– 527. Google Scholar CrossRef Search ADS PubMed 31. Zhang, Y.et al. ( 2014) Multiple receptor tyrosine kinases converge on microRNA-134 to control KRAS, STAT5B and glioblastoma. Cell Death Differ ., 21, 720– 734. Google Scholar CrossRef Search ADS PubMed 32. Zhao, Y.et al. ( 2016) MicroRNA-134 modulates glioma cell U251 proliferation and invasion by targeting KRAS and suppressing the ERK pathway. Tumour Biol ., 37, 11485– 11493. Google Scholar CrossRef Search ADS PubMed 33. Hu, J.et al. ( 2016) MiR-215 is induced post-transcriptionally via HIF-drosha complex and mediates glioma-initiating cell adaptation to hypoxia by targeting KDM1B. Cancer Cell , 29, 49– 60. Google Scholar CrossRef Search ADS PubMed 34. Lujambio, A.et al. ( 2007) Genetic unmasking of an epigenetically silenced microRNA in human cancer cells. Cancer Res ., 67, 1424– 1429. Google Scholar CrossRef Search ADS PubMed 35. Cui, R.et al. ( 2015) MicroRNA-224 promotes tumor progression in nonsmall cell lung cancer. Proc. Natl. Acad. Sci. USA , 112, E4288– E4297. Google Scholar CrossRef Search ADS 36. Tao, S.et al. ( 2014) Oncogenic KRAS confers chemoresistance by upregulating NRF2. Cancer Res ., 74, 7430– 7441. Google Scholar CrossRef Search ADS PubMed 37. Jeuken, J.et al. ( 2007) RAS/RAF pathway activation in gliomas: the result of copy number gains rather than activating mutations. Acta Neuropathol ., 114, 121– 133. Google Scholar CrossRef Search ADS PubMed 38. Wang, C.et al. ( 2014) Beta-naphthoflavone (DB06732) mediates estrogen receptor-positive breast cancer cell cycle arrest through AhR-dependent regulation of PI3K/AKT and MAPK/ERK signaling. Carcinogenesis , 35, 703– 713. Google Scholar CrossRef Search ADS PubMed 39. Hilger, R.A.et al. ( 2002) The Ras-Raf-MEK-ERK pathway in the treatment of cancer. Onkologie , 25, 511– 518. Google Scholar PubMed 40. Manning, B.D.et al. ( 2007) AKT/PKB signaling: navigating downstream. Cell , 129, 1261– 1274. Google Scholar CrossRef Search ADS PubMed 41. Suzuki, H.et al. ( 2015) Mutational landscape and clonal architecture in grade II and III gliomas. Nat. Genet ., 47, 458– 468. Google Scholar CrossRef Search ADS PubMed 42. Song, Y.et al. ( 2013) Evolutionary etiology of high-grade astrocytomas. Proc. Natl. Acad. Sci. USA , 110, 17933– 17938. Google Scholar CrossRef Search ADS 43. Liu, Y.et al. ( 2015) MiR-134 functions as a tumor suppressor in cell proliferation and epithelial-to-mesenchymal Transition by targeting KRAS in renal cell carcinoma cells. DNA Cell Biol ., 34, 429– 436. Google Scholar CrossRef Search ADS PubMed 44. Qiu, Z.A.et al. ( 2016) MicroRNA-134 functions as a tumor suppressor gene in gastric cancer. Am. J. Transl. Res ., 8, 4320– 4328. Google Scholar PubMed 45. Su, X.et al. ( 2017) MicroRNA-134 targets KRAS to suppress breast cancer cell proliferation, migration and invasion. Oncol. Lett ., 13, 1932– 1938. Google Scholar CrossRef Search ADS PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For Permissions, please email: firstname.lastname@example.org.
Carcinogenesis – Oxford University Press
Published: Mar 1, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.
All for just $49/month
Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.
Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.
It’s easy to organize your research with our built-in tools.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera