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Genes 2017, 8(3), 86; doi:10.3390/genes8030086

Systematic Identification and Assessment of Therapeutic Targets for Breast Cancer Based on Genome-Wide RNA Interference Transcriptomes

1
Research Center for Clinical & Translational Medicine, Beijing 302 Hospital, Beijing 100039, China
2
Science and technology on Parallel and Distributed Processing Laboratory, National University of Defense Technology, Changsha 410073, China
3
Huzhou Key Laboratory of Molecular Medicine, Huzhou Central Hospital, Huzhou 313000, China
4
Beijing Institute of Radiation Medicine, Beijing 100850, China
5
No. 451 Hospital of PLA, Xi’an 710054, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Wenyi Gu
Received: 1 December 2016 / Revised: 24 January 2017 / Accepted: 13 February 2017 / Published: 24 February 2017
(This article belongs to the Special Issue RNA Interference 2016)
View Full-Text   |   Download PDF [2386 KB, uploaded 24 February 2017]   |  

Abstract

With accumulating public omics data, great efforts have been made to characterize the genetic heterogeneity of breast cancer. However, identifying novel targets and selecting the best from the sizeable lists of candidate targets is still a key challenge for targeted therapy, largely owing to the lack of economical, efficient and systematic discovery and assessment to prioritize potential therapeutic targets. Here, we describe an approach that combines the computational evaluation and objective, multifaceted assessment to systematically identify and prioritize targets for biological validation and therapeutic exploration. We first establish the reference gene expression profiles from breast cancer cell line MCF7 upon genome-wide RNA interference (RNAi) of a total of 3689 genes, and the breast cancer query signatures using RNA-seq data generated from tissue samples of clinical breast cancer patients in the Cancer Genome Atlas (TCGA). Based on gene set enrichment analysis, we identified a set of 510 genes that when knocked down could significantly reverse the transcriptome of breast cancer state. We then perform multifaceted assessment to analyze the gene set to prioritize potential targets for gene therapy. We also propose drug repurposing opportunities and identify potentially druggable proteins that have been poorly explored with regard to the discovery of small-molecule modulators. Finally, we obtained a small list of candidate therapeutic targets for four major breast cancer subtypes, i.e., luminal A, luminal B, HER2+ and triple negative breast cancer. This RNAi transcriptome-based approach can be a helpful paradigm for relevant researches to identify and prioritize candidate targets for experimental validation. View Full-Text
Keywords: breast cancer; library of integrated network-based cellular signatures; gene set enrichment analysis; drug target; DNA methylation; Cancer Gene Census breast cancer; library of integrated network-based cellular signatures; gene set enrichment analysis; drug target; DNA methylation; Cancer Gene Census
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Liu, Y.; Yin, X.; Zhong, J.; Guan, N.; Luo, Z.; Min, L.; Yao, X.; Bo, X.; Dai, L.; Bai, H. Systematic Identification and Assessment of Therapeutic Targets for Breast Cancer Based on Genome-Wide RNA Interference Transcriptomes. Genes 2017, 8, 86.

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