Identification of Prognostic Genes in Her2-enriched Breast Cancer by Gene Co-Expression Net-work Analysis

Introduction: HER2-enriched subtype of breast cancer has a worse prognosis than luminal subtypes. Recently, the discovery of targeted therapies in other groups of breast cancer has increased patient survival. The aim of this study was to identify genes that affect the overall survival of this group of patients based on a systems biology approach. Methods: Gene expression data and clinical information on 58 patients with HER2-enriched cancer were downloaded from The Cancer Genome Atlas (TCGA). Co-expression modules were identified using the weighted gene co-expression network analysis (WGCNA). The Cox regression was used to determine the modules that had a significant relationship with the overall survival (OS) endpoint. Single-gene survival analysis was performed within the selected module. Finally, functional annotation to explore the significance of genes was performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Results: Of the six identified co-expression modules, two had significantly poor prognoses. Single-gene survival analysis showed that 39% of genes in the selected modules were identified as significant. The genes were mainly related to the biological pathways such as Ubiquitin-mediated proteolysis and RNA degradation. CHAMP1, PPP1R26, PRRC2B, KANSL3, and ANAPC2 were identified as the 5 most important genes associated with reduced OS, in order of significance. Conclusion: The systems biology approach can provide appropriate results relate to patient survival analysis. In this study, some genes were identified to be used as prognostic biomarkers in experimental studies related to the OS in the HER2-enriched subgroup. These genes can be considered potential candidates for therapeutic targets in this group of patients.