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

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.