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Current Bioinformatics

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ISSN (Print): 1574-8936
ISSN (Online): 2212-392X

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Research Article

Analyzing Association Between Expression Quantitative Trait and CNV for Breast Cancer Based on Gene Interaction Network Clustering and Group Sparse Learning

Author(s): Xia Chen, Yexiong Lin, Qiang Qu, Bin Ning, Haowen Chen*, Bo Liao and Xiong Li

Volume 17, Issue 4, 2022

Published on: 06 April, 2022

Page: [358 - 368] Pages: 11

DOI: 10.2174/1574893617666220207095117

Price: $65

Abstract

Aim: The occurrence and development of tumor are accompanied by a change in pathogenic gene expression. Tumor cells avoid the damage of immune cells by regulating the expression of immune- related genes.

Background: Tracing the causes of gene expression variation is helpful to understand tumor evolution and metastasis.

Objective: Current explanation methods for gene expression variation are confronted with several main challenges, which include low explanation power, insufficient prediction accuracy, and lack of biological meaning.

Methods: In this study, we propose a novel method to analyze the mRNA expression variations of breast cancer risk genes. Firstly, we collected some high-confidence risk genes related to breast cancer and then designed a rank-based method to preprocess the breast cancers copy number variation (CNV) and mRNA data. Secondly, to elevate the biological meaning and narrow down the combinatorial space, we introduced a prior gene interaction network and applied a network clustering algorithm to generate high-density subnetworks. Lastly, to describe the interlinked structure within and between subnetworks and target genes mRNA expression, we proposed a group sparse learning model to identify CNVs for pathogenic genes expression variations.

Results: The performance of the proposed method is evaluated by both significantly improved predication accuracy and biological meaning of pathway enrichment analysis.

Conclusion: The experimental results show that our method has practical significance.

Keywords: Breast cancer, gene expression regulation analysis, regression model, regularization method, mRNA, NGS.

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