Literature DB >> 30470304

Maximizing the Utility of Cancer Transcriptomic Data.

Yu Xiang1, Youqiong Ye1, Zhao Zhang2, Leng Han3.   

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.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

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

Mesh：

Substances：

Year:  2018        PMID： 30470304     DOI： 10.1016/j.trecan.2018.09.009

Source DB:  PubMed          Journal:  Trends Cancer        ISSN： 2405-8025


  10 in total

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  10 in total

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