Applications of RNA Indexes for Precision Oncology in Breast Cancer

• Published in: Genomics, proteomics & bioinformatics Vol. 16; no. 2; pp. 108 - 119
• Main Authors: Ma, Liming, Liang, Zirui, Zhou, Hui, Qu, Lianghu
• Format: Journal Article
• Language: English
• Published: England Elsevier B.V 01.04.2018; Key Laboratory of Gene Engineering of the Ministry of Education,State Key Laboratory of Biocontrol,School of Life Sciences,Sun Yat-sen University,Guangzhou 510275,China; Elsevier; Oxford University Press
• Subjects: Breast cancer; Female; Gene Expression Profiling; Genomics; Humans; microRNA; MicroRNAs - metabolism; Precision Medicine; Precision oncology; Review; RNA Interference; RNA, Messenger - metabolism; Transcriptomics; Triple Negative Breast Neoplasms - classification; Triple Negative Breast Neoplasms - genetics; Triple Negative Breast Neoplasms - metabolism; Triple Negative Breast Neoplasms - therapy; microRNA; Breast cancer; RNA interference; Precision oncology; Transcriptomics
• ISSN: 1672-0229; 2210-3244
• DOI: 10.1016/j.gpb.2018.03.002

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology.