Access the full text.
Sign up today, get DeepDyve free for 14 days.
Summary: The availability of cancer genomic data makes it possible to analyze genes related to cancer. Cancer is usually the result of a set of genes and the signal of a single gene could be covered by background noise. Here, we present a web server named Gene Set Cancer Analysis (GSCALite) to analyze a set of genes in cancers with the following functional modules. (i) Differential expression in tumor versus normal, and the survival analysis; (ii) Genomic variations and their survival analysis; (iii) Gene expression associated cancer pathway activity; (iv) miRNA regulatory network for genes; (v) Drug sensitivity for genes; (vi) Normal tissue expression and eQTL for genes. GSCALite is a user-friendly web server for dynamic analysis and visualization of gene set in cancer and drug sensitivity correlation, which will be of broad utilities to cancer researchers. Availability and implementation: GSCALite is available on http://bioinfo.life.hust.edu.cn/web/ GSCALite/. Contact: firstname.lastname@example.org or email@example.com Supplementary information: Supplementary data are available at Bioinformatics online. 1 Introduction gene expression and survival. However, cancer initiation, progres- Next generation sequencing (NGS) technology has emerged as a sion and metastasis are inclined to the result of mutation and/or ex- powerful method for cancer genomics analysis (Ding et al., 2014). pression alterations of a set of genes or pathways (Harvey et al., The Cancer Genome Atlas (TCGA) (Weinstein et al., 2013), 2013). Thus, performing gene set association analysis with big data Genotype-Tissue Expression (GTEx) (GTEx Consortium, 2015) and of cancer multi-omics and drug sensitivity is imperative and very other projects have generated a large amount of complex, multi- useful for cancer research. Therefore, we developed an interactive omics data for cancer and normal samples. These publicly available web-based application named GSCALite for Gene Set Cancer datasets provide unprecedented opportunities to understand cancer Analysis to analyze and visualize the expression/variation/correl- causal genes and mechanism, find candidate drug targets, and screen ation of a gene set in cancers with flexible manner. GSCALite offers genes associated with phenotypes. Recently, a few excellent web analyses including gene differential expression, overall survival, sin- servers such as cBioPortal focusing on the genomic variations based gle nucleotide variation, copy number variation, methylation, path- on multi-omics (Cerami1 et al., 2012), GEPIA (Tang et al., 2017) way activity, miRNA regulation, normal tissue expression and drug and Oncomine (Rhodes et al., 2007) providing analysis for single sensitivity. GSCALite provided various publication-ready figures V The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: firstname.lastname@example.org 3771 3772 C.-J.Liu et al. and tables for users and the workflow was used in our recent paper 2.3 Expression profiling and eQTL in normal tissues (Gong et al., 2017). In brief, we integrated big multi-omics and drug GSCALite provides GTEx Normal Tissue module for gene set tissue data to provide all-in-one analysis for a set of genes in cancers. specificity analysis. This analysis offers a comprehensive display of expression profiling and eQTL information for gene set in selected normal tissues. After analysis of this module, GSCALite provides a 2 Methods and functions heatmap plot for selected tissues with expression value of each gene normalized by the median. The user-interface and back-end of GSCALite were written in Shiny. GSCALite consists of analytic modules for data from three major sources including multi-omics data from TCGA 11 160 samples 3 Discussion across 33 cancer types (TCGA Cancer), 746 drug data from The GSCALite provides foundational tools and workflows in an all- Genomics of Drug Sensitivity in Cancer (GDSC) (Yang et al.,2013) in-one platform for cancer genomics analysis for a set of genes. and Cancer Therapeutics Response Portal (CTRP) (Basu et al.,2013) GSCALite is a time-saving and intuitive tool for unleashing the value (Drug Sensitivity), and normal tissue expression data of 11 688 sam- of the cancer genomics big data which enables experimental biolo- ples from GTEx (GTEx Normal Tissue). We used R scripts and pack- gists without any computational programming skills to test hypoth- ages (ggplot2, visNetwork, survival and maftools) to generate figures esis. It is based on gene set analysis with multi-omics data which and tables (details refer to the web site help pages). Analysis results complements the analysis with mRNA expression alone or single are returned to the web page and can be downloaded in PDF, PNG, gene analysis. We will maintain the GSCALite web server for at least EPS, TXT as well as HTML formats. The workflow and typical out- 5 years and update it with cancer genomics data increasing and new put schema are shown in Supplementary Figure S1. Detailed functions methods development. We anticipate GSCALite to help cancer re- and operations for each module are described below. search community and aid discovery of cancer pathways and drugs. 2.1 Gene set based multi-omics cancer analysis GSCALite provides the following six analysis modules for a gene set Funding based on TCGA multi-omics cancer data: This work has been supported by The National Key Research and i. mRNA Expression module calculates the gene set differential Development Program of China (2017YFA0700403) and National Natural Science Foundation of China (Nos. 31471247 and 31771458). expression between tumor and paired normal samples, the im- pact of gene expression to overall survival and expression differ- Conflict of Interest: none declared. ence between subtypes in each selected cancer type. ii. Single Nucleotide Variation module uses maftools (Mayakonda and Koefﬂer, 2016) to present the SNV frequency and variant References types of the gene set in selected cancer types. The effects of muta- Akbani,R. et al. (2014) A pan-cancer proteomic perspective on The Cancer tions to overall survival are given by means of the log-rank test Genome Atlas. Nat. Commun., 5, 3887. which facilitate to evaluate the prognosis of the gene set mutations. Basu,A. et al. (2013) An interactive resource to identify cancer genetic and lin- iii. On Copy Number Variation module, the statistics of heterozygous eage dependencies targeted by small molecules. Cell, 154, 1151–1161. and homozygous CNV of each cancer type are displayed as pie Cerami1,E. (2012) The cBio Cancer Genomics Portal: an open platform for chat for gene set, and Pearson correlation is performed between exploring multidimensional cancer genomics data. Cancer Discov., 2, 401–404. gene expression and CNV of each gene in each cancer to help to Ding,L. et al. (2014) Expanding the computational toolbox for mining cancer analyze the gene expression signiﬁcantly affected by CNV. genomes. Nat. Rev. Genet., 15, 556–570. iv. Methylation module explores the differential methylation be- Gong,J. et al. (2017) A pan-cancer analysis of the expression and clinical rele- tween tumor and paired normal samples, the correlation of vance of small nucleolar RNAs in human cancer. Cell Rep., 21, 1968–1981. methylation and expression, and the survival affected by methy- GTEx Consortium (2015) The Genotype-Tissue Expression (GTEx) pilot ana- lation level for selected cancer types. lysis: multitissue gene regulation in humans. Science, 348, 648–660. v. Pathway Activity module presents the correlation of genes ex- Harvey,K.F. et al. (2013) The Hippo pathway and human cancer. Nat. Rev. pression with pathway activity groups (activation and inhib- Cancer, 13, 246–257. ition) that deﬁned by pathway scores (Akbani et al., 2014). Mayakonda,A. and Koefﬂer,H.P. (2016) Maftools: efﬁcient analysis, visual- vi. For miRNA regulations, miRNA Network module combines ization and summarization of MAF ﬁles from large-scale cohort based can- cer studies. bioRxiv, 052662. miRNA targeting data from veriﬁed target databases and predic- Rhodes,D.R. et al. (2007) Oncomine 3.0: genes, pathways, and networks in a tion methods as our previous studies (Zhang et al.,2015, 2016), collection of 18,000 cancer gene expression proﬁles. Neoplasia, 9, 166–180. and the negative correlation with gene expressions to explore the Tang,Z. et al. (2017) GEPIA: a web server for cancer and normal gene expres- miRNA-gene regulatory network for gene set in all cancer types. sion proﬁling and interactive analyses. Nucleic Acids Res., 45, W98–W102. Weinstein,J.N. et al. (2013) The cancer genome atlas pan-cancer analysis pro- ject. Nat. Genet., 45, 1113–1120. 2.2 The analysis of drug sensitivity and resistance Yang,W. et al. (2013) Genomics of Drug Sensitivity in Cancer (GDSC): a re- to genes source for therapeutic biomarker discovery in cancer cells. Nucleic Acids Genomic aberrations influence clinical responses to treatment and are Res., 41, D955–D961. potential biomarkers for drug screening. Drug sensitivity and gene ex- Zhang,H.-M. et al. (2016) miR-146b-5p within BCR-ABL1–positive microve- pression profiling data of cancer cell lines in GDSC and CTRP were sicles promotes leukemic transformation of hematopoietic cells. Cancer integrated into GSCALite. The expression of each gene in the gene set Res., 76, 2901–2911. was performed by Spearman correlation analysis with the small mol- Zhang,H.-M. et al. (2015) Transcription factor and microRNA co-regulatory ecule/drug sensitivity (IC50). Correlations with false discovery rate loops: important regulatory motifs in biological processes and diseases. (FDR< 0.05) were filtered as significant ones. Brief. Bioinform., 16, 45–58.
Bioinformatics – Oxford University Press
Published: May 22, 2018
Access the full text.
Sign up today, get DeepDyve free for 14 days.