Using machine learning to identify gene interaction networks associated with breast cancer

Liyuan Liu; Wenli Zhai; Fei Wang; Lixiang Yu; Fei Zhou; Yujuan Xiang; Shuya Huang; Chao Zheng; Zhongshang Yuan; Yong He; Zhigang Yu; Jiadong Ji

doi:10.1186/s12885-022-10170-w

Using machine learning to identify gene interaction networks associated with breast cancer

BMC Cancer. 2022 Oct 17;22(1):1070. doi: 10.1186/s12885-022-10170-w.

Authors

Liyuan Liu^#^{1

2}, Wenli Zhai^#³, Fei Wang^{1

4}, Lixiang Yu^{1

4}, Fei Zhou^{1

4}, Yujuan Xiang^{1

4}, Shuya Huang^{1

4}, Chao Zheng^{1

4}, Zhongshang Yuan⁵, Yong He³, Zhigang Yu^{6

7}, Jiadong Ji⁸

Affiliations

¹ Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, 250033, Jinan, China.
² School of Mathematics, Shandong University, Jinan, 250100, China.
³ Institute for Financial Studies, Shandong University, Jinan, 250100, China.
⁴ Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, 250100, China.
⁵ Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
⁶ Department of Breast Surgery, The Second Hospital, Cheeloo College of Medicine, Shandong University, 250033, Jinan, China. yuzhigang@sdu.edu.cn.
⁷ Institute of Translational Medicine of Breast Disease Prevention and Treatment, Shandong University, Jinan, 250100, China. yuzhigang@sdu.edu.cn.
⁸ Institute for Financial Studies, Shandong University, Jinan, 250100, China. jiadong@sdu.edu.cn.

^# Contributed equally.

Abstract

Background: Breast cancer (BC) is one of the most prevalent cancers worldwide but its etiology remains unclear. Obesity is recognized as a risk factor for BC, and many obesity-related genes may be involved in its occurrence and development. Research assessing the complex genetic mechanisms of BC should not only consider the effect of a single gene on the disease, but also focus on the interaction between genes. This study sought to construct a gene interaction network to identify potential pathogenic BC genes.

Methods: The study included 953 BC patients and 963 control individuals. Chi-square analysis was used to assess the correlation between demographic characteristics and BC. The joint density-based non-parametric differential interaction network analysis and classification (JDINAC) was used to build a BC gene interaction network using single nucleotide polymorphisms (SNP). The odds ratio (OR) and 95% confidence interval (95% CI) of hub gene SNPs were evaluated using a logistic regression model. To assess reliability, the hub genes were quantified by edgeR program using BC RNA-seq data from The Cancer Genome Atlas (TCGA) and identical edges were verified by logistic regression using UK Biobank datasets. Go and KEGG enrichment analysis were used to explore the biological functions of interactive genes.

Results: Body mass index (BMI) and menopause are important risk factors for BC. After adjusting for potential confounding factors, the BC gene interaction network was identified using JDINAC. LEP, LEPR, XRCC6, and RETN were identified as hub genes and both hub genes and edges were verified. LEPR genetic polymorphisms (rs1137101 and rs4655555) were also significantly associated with BC. Enrichment analysis showed that the identified genes were mainly involved in energy regulation and fat-related signaling pathways.

Conclusion: We explored the interaction network of genes derived from SNP data in BC progression. Gene interaction networks provide new insight into the underlying mechanisms of BC.

Keywords: Breast cancer; Differential network analysis; Gene interaction network; Single nucleotide polymorphism.

MeSH terms

Breast Neoplasms* / pathology
Female
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
Humans
Machine Learning
Obesity / genetics
Polymorphism, Single Nucleotide
Reproducibility of Results

Abstract

MeSH terms

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