Maximizing the Utility of Cancer Transcriptomic Data

Trends Cancer. 2018 Dec;4(12):823-837. doi: 10.1016/j.trecan.2018.09.009. Epub 2018 Oct 14.

Abstract

Transcriptomic profiling has been applied to large numbers of cancer samples, by large-scale consortia, including The Cancer Genome Atlas, International Cancer Genome Consortium, and Cancer Cell Line Encyclopedia. Advances in mining cancer transcriptomic data enable us to understand the endless complexity of the cancer transcriptome and thereby to discover new biomarkers and therapeutic targets. In this paper, we review computational resources for deep mining of transcriptomic data to identify, quantify, and determine the functional effects and clinical utility of transcriptomic events, including noncoding RNAs, post-transcriptional regulation, exogenous RNAs, and transcribed genetic variants. These approaches can be applied to other complex diseases, thereby greatly leveraging the impact of this work.

Keywords: cancer transcriptome; exogenous RNA; noncoding RNA; post-transcriptional regulation; transcribed genetic variant.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.
  • Review

MeSH terms

  • Antineoplastic Agents / pharmacology
  • Antineoplastic Agents / therapeutic use
  • Biomarkers, Tumor / antagonists & inhibitors
  • Biomarkers, Tumor / genetics
  • Databases, Genetic / statistics & numerical data*
  • Gene Expression Profiling / statistics & numerical data*
  • Gene Expression Regulation, Neoplastic*
  • Gene Regulatory Networks / drug effects
  • Gene Regulatory Networks / genetics
  • Humans
  • Neoplasms / drug therapy
  • Neoplasms / genetics*
  • Transcriptome / drug effects
  • Transcriptome / genetics

Substances

  • Antineoplastic Agents
  • Biomarkers, Tumor