OncoLnc: linking TCGA survival data to mRNAs, miRNAs, and lncRNAs

OncoLnc is a tool for interactively exploring survival correlations, and for downloading clinical data coupled to expression data for mRNAs, miRNAs, or long noncoding RNAs (lncRNAs). OncoLnc contains survival data for 8,647 patients from 21 cancer studies performed by The Cancer Genome Atlas (TCGA), along with RNA-SEQ expression for mRNAs and miRNAs from TCGA, and lncRNA expression from MiTranscriptome beta. Storing this data gives users the ability to separate patients by gene expression, and then create publication-quality Kaplan-Meier plots or download the data for further analyses. OncoLnc also stores precomputed survival analyses, allowing users to quickly explore survival correlations for up to 21 cancers in a single click. This resource allows researchers studying a specific gene to quickly investigate if it may have a role in cancer, and the supporting data allows researchers studying a specific cancer to identify the mRNAs, miRNAs, and lncRNAs most correlated with survival, and researchers looking for a novel lncRNA involved with cancer lists of potential candidates. OncoLnc is available at http://www.oncolnc.org. Subjects Bioinformatics, Computational Biology, Databases

[1]  Wen-chang Lin,et al.  Bioinformatic Interrogation of 5p-arm and 3p-arm Specific miRNA Expression Using TCGA Datasets , 2015, Journal of clinical medicine.

[2]  S. Dhanasekaran,et al.  The landscape of long noncoding RNAs in the human transcriptome , 2015, Nature Genetics.

[3]  Benjamin J. Raphael,et al.  Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin , 2014, Cell.

[4]  Christian A. Rees,et al.  Molecular portraits of human breast tumours , 2000, Nature.

[5]  Wei-Min Chen,et al.  A pan-cancer analysis of prognostic genes , 2015 .

[6]  R. Verhaak,et al.  The Pan-Cancer Analysis of Pseudogene Expression Reveals Biologically and Clinically Relevant Tumour Subtypes , 2014, Nature Communications.

[7]  Yan Zhang,et al.  CanPredict: a computational tool for predicting cancer-associated missense mutations , 2007, Nucleic Acids Res..

[8]  Leng Han,et al.  Gene co-expression network analysis reveals common system-level properties of prognostic genes across cancer types , 2014, Nature Communications.

[9]  Chun-Ming Tsai,et al.  Should EGFR mutations be tested in advanced lung squamous cell carcinomas to guide frontline treatment? , 2014, Cancer Chemotherapy and Pharmacology.

[10]  Richard Simon,et al.  Identifying cancer driver genes in tumor genome sequencing studies , 2011, Bioinform..

[11]  Leyla Isik,et al.  Cancer-specific high-throughput annotation of somatic mutations: computational prediction of driver missense mutations. , 2009, Cancer research.

[12]  I. Kurochkin,et al.  Long noncoding RNAs: a potential novel class of cancer biomarkers , 2015, Front. Genet..

[13]  Andrea Bild,et al.  Alternative preprocessing of RNA-Sequencing data in The Cancer Genome Atlas leads to improved analysis results , 2015, Bioinform..

[14]  Jan Budczies,et al.  Online Survival Analysis Software to Assess the Prognostic Value of Biomarkers Using Transcriptomic Data in Non-Small-Cell Lung Cancer , 2013, PloS one.

[15]  Chris Sander,et al.  Emerging landscape of oncogenic signatures across human cancers , 2013, Nature Genetics.

[16]  Jana Jeschke,et al.  MEXPRESS: visualizing expression, DNA methylation and clinical TCGA data , 2015, BMC Genomics.

[17]  Benjamin E. Gross,et al.  The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. , 2012, Cancer discovery.

[18]  Paul L. Roebuck,et al.  TANRIC: An Interactive Open Platform to Explore the Function of lncRNAs in Cancer. , 2015, Cancer research.

[19]  D. Haussler,et al.  The Somatic Genomic Landscape of Glioblastoma , 2013, Cell.