Abstract 2481: Developing analysis platform for pan-cancer study of DNA methylation, mirna and lncrna expression based on tumor subtypes using TCGA data
Authors: Chandrashekar Darshan Shimoga , Creighton Chad J. , Ponce-Rodriguez Israel , Varambally Sooryanarayana 
Published: August 2019

Abstract
Introduction: The Cancer Genome Atlas (TCGA) consortium performed high-throughput sequencing of thousands of tumor and normal tissues across 33 cancer types. This has led to molecular characterization of different cancers and many novel discoveries. Such diverse data offers an excellent opportunity to further address the questions associated with tumor heterogeneity. Systematic exploration of epigenetic features and non-coding gene expression could lead clinicians/cancer researchers towards unearthing new diagnostic biomarkers and therapeutic targets. Previously our group developed a cancer transcriptome web portal, UALCAN [http://ualcan.path.uab.edu, Google: UALCAN)] to study expression and survival profile of protein coding genes among TCGA cancers based on various subgroups and molecular subtypes of cancer. Although many data portals facilitating easy access and in-depth analysis of TCGA data exists, there is need for a user-friendly resource facilitating comprehensive DNA methylation profile and gene expression analysis of lncRNA and miRNA within tumor subgroups based on factors such as stage, tumor grade, age, sex, race and integration with the gene expression.
Methods: TCGA Level 3 RNA-seq, miRNA-seq and Illumina Infinium HumanMethylation450K data were downloaded using TCGA-Assembler 2 tool and Genomic Data Commons (GDC). The data were processed, organized based on tumor subtypes using custom R scripts. DNA methylation and gene expression analysis was performed using in-house PERL scripts, while statistical significance was estimated using “Statistics::TTest” module. The analyses results were stored as flat file database and hosted on a web portal developed using Apache2.0, PERL-CGI and HighChartJS JavaScript libraries.
Results: The user friendly web portal 1) allows users could obtain list of top over-/under-expressed miRNAs and lncRNAs in each of 33 TCGA cancer 2) facilitates analysis of promoter methylation level and gene expression for given set of miRNA/ lncRNAs in various tumor sub-groups based on individual cancer stages, tumor grade, race, body weight or other clinicopathologic features, 3) enables researchers to download high resolution graphics such as
heatmap, boxplots, dotplots as analyses outputs. The data can be downloaded in multiple output formats for use by researchers.
Conclusion: The current resource aids cancer researchers in identifying DNA methylation mediated epigenetic modification of gene expression in tumor subgroup specific manner. Furthermore, our platform will facilitate analyses of long non-coding RNAs and microRNAs, thereby help in discovery of novel biomarkers and better understanding of the tumor biology.
Citation Format: Darshan Shimoga Chandrashekar, Chad J. Creighton, Israel Ponce-Rodriguez, Sooryanarayana Varambally. Developing analysis platform for pan-cancer study of DNA methylation, mirna and lncrna expression based on tumor subtypes using TCGA data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 2481.