| Abstrakt: |
Breast cancer remains a leading cause of mortality among women, characterized by its complex and diverse nature, comprising various molecular subtypes, each demonstrating distinct treatment responses and clinical outcomes. Addressing the complexity of tumor heterogeneity and advancing precision medicine are key objectives in current breast cancer research. This study aims to deepen understanding of the disease complexities at the molecular subtype level. RNA sequencing data for luminal A, luminal B, HER2-positive, and triple-negative breast cancer (TNBC) subtypes, with corresponding normal samples, were obtained from the Gene Expression Omnibus (GEO) database. We identified modules containing unique and significantly correlated genes with each subtype using weighted gene co-expression network analysis (WGCNA). Subsequently, genes within each subtype-specific module were explored by constructing and analyzing protein–protein interaction (PPI) networks. The prognostic and diagnostic potential of the identified crucial proteins for each subtype was evaluated through survival and ROC curve analyses. We identified crucial proteins, NSF, FKBP4, NTRK2, and SLC7A5 within the luminal A associated module, GYPC, ALDH18A1, and KLF2 specific to luminal B module, AURKA, BUB1, PLK1, ANLN, BIRC5, RAD51, and SQLE specific to TNBC module, exhibited significant associations with adverse survival outcomes in breast cancer patients and also demonstrated remarkable diagnostic efficacy in differentiating tumor and normal samples. The present study offers new insights for advancing research on breast cancer subtypes. |
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