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

Maximizing the Utility of Cancer Transcriptomic Data.

Author information

1
Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; These authors contributed equally.
2
Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
3
Department of Biochemistry and Molecular Biology, McGovern Medical School at The University of Texas Health Science Center at Houston, Houston, TX 77030, USA; Center for Precision Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA. Electronic address: leng.han@uth.tmc.edu.

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

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