Integrated Bioinformatics Identification of Hub Genes Associated with Breast Cancer Progression

Breast cancer is one of the most common malignancies affecting women worldwide and remains a major cause of cancer-related mortality. The present study aimed to identify significant hub genes and molecular pathways associated with breast cancer progression using an integrated bioinformatics approach. The gene expression dataset GSE15852 was obtained from the Gene Expression Omnibus (GEO) database and analyzed using GEO2R to identify differentially expressed genes between normal and breast cancer samples. Protein–protein interaction network analysis was performed using the STRING database, followed by hub gene identification and functional enrichment analysis using GO and KEGG pathways. Several significant genes were identified, among which MUC1, KRT19, KRT18, GATA3 and EPCAM were recognized as candidate hub genes associated with breast cancer progression. Functional enrichment analysis revealed that these genes were mainly involved in transcription regulation, epithelial differentiation and cancer-related signaling pathways. The findings of the present study suggest that the identified hub genes may serve as candidate biomarkers for breast cancer progression and may contribute to future diagnostic and therapeutic research.