Integrative analyses of radiation-related genes and biomarkers associated with breast cancer

OBJECTIVE: In addition to significantly reducing breast cancer recurrence risk, radiotherapy also prolongs patients' lives. However, radiotherapy-related genes and biomarkers still remain poorly understood. The present study aimed to identify radiation-associated genes in breast cancer. MATERIALS AND METHODS: Breast cancer data were downloaded from Gene Expression Omnibus (GEO) and UCSC Xena database. The gene ontology (GO) enrichment and gene set enrichment analysis (GSEA) were performed for annotation and integrated discovery. Protein-protein interaction (PPI) network was constructed by STRING database and hub genes were identified. Then, immunohistochemistry and tissue expression of key genes was analyzed by using the Human Protein Atlas (HPA) and GEPIA database. Genes associated with prognosis were identified by performing univariate cox analysis. RESULTS: We identified 341 differentially expressed genes related to radiotherapy in breast cancer patients. PPI analysis revealed a total of 129 nodes and 516 interactions and identified five hub genes (EGFR, FOS, ESR1, JUN, and IL6). In addition, 11 SDEGs THBS1, SERPINA11, NFIL3, METTL7A, KCTD12, HSPA6, EGR1, DDIT4, CCDC3, C11orf96, and BCL2A1 candidate genes can be used as potential diagnostic markers. The calibration curve and ROC indicate good probability consistencies of 3-years and 5-year survival rates of patients between estimation and observation. CONCLUSIONS: Our findings provide novel insight into the functional characteristics of breast cancer through integrative analysis of GEO data and suggest potential biomarkers and therapeutic targets for breast cancer.