Article

Identification of important genes and drug repurposing based on clinical-centered analysis across human cancers

Authors: Ying Li1, Ya-ping Dong1,2, You-wen Qian3, Le-xing Yu1, Wen Wen1,4, Xiu-liang Cui1,4, Hong-yang Wang1,4,5
1 National Center for Liver Cancer, Second Military Medical University, Shanghai 200438, China
2 The Graduate School of Fujian Medical University, Fuzhou 350122, China
3 Department of Pathology, Eastern Hepatobiliary Surgery Hospital/Institute, Second Military Medical University, Shanghai 200433, China
4 International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital/Institute, Second Military Medical University, Shanghai 200433, China
5 Ministry of Education (MOE) Key Laboratory on Signaling Regulation and Targeting Therapy of Liver Cancer, Second Military Medical University, Shanghai 200433, China
Correspondence to: Xiu-liang Cui: wafyai@163.com, Hong-yang Wang: hywangk@vip.sina.com,
DOI: 10.1038/s41401-020-0451-1
Received: 31 December 2019
Accepted: 30 May 2020
Advance online: 18 June 2020

Abstract

Identification of the functional impact of mutated and altered genes in cancer is critical for implementing precision oncology and drug repurposing. In recent years, the emergence of multiomics data from large, well-characterized patient cohorts has provided us with an unprecedented opportunity to address this problem. In this study, we investigated survival-associated genes across 26 cancer types and found that these genes tended to be hub genes and had higher K-core values in biological networks. Moreover, the genes associated with adverse outcomes were mainly enriched in pathways related to genetic information processing and cellular processes, while the genes with favorable outcomes were enriched in metabolism and immune regulation pathways. We proposed using the number of survival-related neighbors to assess the impact of mutations. In addition, by integrating other databases including the Human Protein Atlas and the DrugBank database, we predicted novel targets and anticancer drugs using the drug repurposing strategy. Our results illustrated the significance of multidimensional analysis of clinical data in important gene identification and drug development.
Keywords: clinical correlation; survival outcomes; somatic mutation; drug repurposing; network analysis; pancancer.

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