Identification of cancer driver genes based on hierarchical weak consensus model

Gaoshi Li; Zhipeng Hu; Xinlong Luo; Jiafei Liu; Jingli Wu; Wei Peng; Xiaoshu Zhu

doi:10.1007/s13755-024-00279-6

Identification of cancer driver genes based on hierarchical weak consensus model

Health Inf Sci Syst. 2024 Mar 6;12(1):21. doi: 10.1007/s13755-024-00279-6. eCollection 2024 Dec.

Authors

Gaoshi Li^{1

2

3}, Zhipeng Hu^{1

2

3}, Xinlong Luo^{1

2

3}, Jiafei Liu^{1

2

3}, Jingli Wu^{1

2

3}, Wei Peng⁴, Xiaoshu Zhu^{1

2

3

5}

Affiliations

¹ Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, Guangxi Normal University, Guilin, 541004 China.
² Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, Guilin, 541004 Guangxi China.
³ College of Computer Science and Engineering, Guangxi Normal University, Guilin, 541004 Guangxi China.
⁴ Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming, 650500 Yunnan China.
⁵ School of Computer and Information Security & School of Software Engineering, Guilin University of Electronic Science and Technology, Guilin, China.

PMID: 38464463
PMCID: PMC10917728 (available on 2025-12-01)
DOI: 10.1007/s13755-024-00279-6

Abstract

Cancer is a complex gene mutation disease that derives from the accumulation of mutations during somatic cell evolution. With the advent of high-throughput technology, a large amount of omics data has been generated, and how to find cancer-related driver genes from a large number of omics data is a challenge. In the early stage, the researchers developed many frequency-based driver genes identification methods, but they could not identify driver genes with low mutation rates well. Afterwards, researchers developed network-based methods by fusing multi-omics data, but they rarely considered the connection among features. In this paper, after analyzing a large number of methods for integrating multi-omics data, a hierarchical weak consensus model for fusing multiple features is proposed according to the connection among features. By analyzing the connection between PPI network and co-mutation hypergraph network, this paper firstly proposes a new topological feature, called co-mutation clustering coefficient (CMCC). Then, a hierarchical weak consensus model is used to integrate CMCC, mRNA and miRNA differential expression scores, and a new driver genes identification method HWC is proposed. In this paper, the HWC method and current 7 state-of-the-art methods are compared on three types of cancers. The comparison results show that HWC has the best identification performance in statistical evaluation index, functional consistency and the partial area under ROC curve.

Supplementary information: The online version contains supplementary material available at 10.1007/s13755-024-00279-6.

Keywords: Cancer; Driver genes; Hierarchical weak consensus model; Hypergraph; Multi-omics data.

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