Identification of Diagnostic and Prognostic Subnetwork Biomarkers for Women with Breast Cancer Using Integrative Genomic and Network-Based Analysis

Breast cancer remains a major global health concern and a leading cause of cancer-related deaths among women. Early detection and effective treatment are essential in improving patient survival. Advances in omics technologies have provided deeper insights into the molecular mechanisms underlying breast cancer. This study aimed to identify subnetwork markers with diagnostic and prognostic potential by integrating genome-wide gene expression data with protein-protein interaction networks. We identified four significant subnetworks revealing potentially important hub genes, including <i>VEGFA</i>, <i>KIF4A</i>, <i>ZWINT</i>, <i>PTPRU</i>, <i>IKBKE</i>, <i>STYK1</i>, <i>CENPO</i>, and <i>UBE2C</i>. The diagnostic and prognostic potentials of these subnetworks were validated using independent datasets. Unsupervised principal component analysis demonstrated a clear separation of breast cancer patients from healthy controls across multiple datasets. A KNN classification model, based on these subnetworks, achieved an accuracy of 97%, sensitivity of 98%, specificity of 94%, and area under the curve (AUC) of 96%. Moreover, the prognostic significance of these subnetwork markers was validated using independent transcriptomic datasets comprising over 4000 patients. These findings suggest that subnetwork markers derived from integrated genomic network analyses can enhance our understanding of the molecular landscape of breast cancer, potentially leading to improved diagnostic, prognostic, and therapeutic strategies.