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

Abstract

Aims: Aims: The occurrence and development of tumor is accompanied by the change of pathogenic gene expression. Tumor cells avoid the damage of immune cells by regulating the expression of immune related genes. Background: Background: Tracing the causes of gene expression variation is helpful to understand tumor evolution and metastasis. Objective: Objective: Current gene expression variation explanation methods are confronted with several main challenges: low explanation power, insufficient prediction accuracy, and lack of biological meaning. Method: Method: In this study, we propose a novel method to analyze the mRNA expression variations of breast cancers 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. Result: Result: The performance of the proposed method is evaluated by both significantly improved predication accuracy and biological meaning of pathway enrichment analysis. Conclusion: Conclusion: The experimental results show that our method has practical significance