Identification of New Key Genes in Breast Cancer by Co-expression Network Analysis

Abstract

Background: Breast cancer is one of the most common malignancies in women all over the world. This study aimed to identify the potential biomarkers associated with the occurrence and development of breast cancer.Results: Our research downloaded GSE54140 gene expression datasets and GPL10152 platform information from the Gene Expression Omnibus datasets, and used weighted gene co-expression network analysis (WGCNA) to construct a scale-free gene co-expression network to explore the associations between gene sets and clinical features. A total of 60 modules were analyzed, and found that the skyblue3 module was significantly related to HER2+ BC. The function of 93 genes in the skyblue3 module was annotated by DAVID bioinformatics tool, and it was demonstrated that the function of the module was mainly related to nuclear-transcribed mRNA catabolic process, cytosol, and oxidoreductase activity. Based on the WGCNA and Cytoscape software analysis, 9 hub genes (PGAP3, PPP1R1B, PNMT, ERBB2, CISD3, CRKRS, TCAP, STARD3, and NEUROD2) were identified. The Human Protein Atlas database detected that the protein level of PGAP3, PPP1R1B, PNMT, ERBB2, CISD3, CRKRS, TCAP, and STARD3 gene in tumor tissues was significantly higher than those in normal tissues. And survival analysis shows that PGAP3, PNMT, ERBB2, TCAP, and STARD3 were negatively associated with the overall survival (P < 0.05).Conclusion: A total of 9 candidate biomarkers were identified by comprehensive bioinformatics analysis, among which, the co-expansion of PGAP3 and CRKRS related to ERBB2 may be associated with the occurrence of breast cancer. In addition, PPP1R1B, CRKRS and TCAP are related to drug resistance and adverse reactions in the treatment of breast cancer.