PS4-05-17: Identifying hub genes among different breast cancer subtypes and racial diversity through integrated bioinformatics analysis (WGCNA)

Abstract Introduction: Focusing only on driver genes may ignore important interactions and pathways in breast cancer across subtypes and racial groups. Understanding their dysfunctional network or hub genes is crucial, as these are related to mRNA expression patterns. Our goal is to identify key hub genes related to race and subtype. Methods and materials: RNA-seq counts and clinical data from TCGA (GSE62944) were filtered to include only invasive breast cancer cases with complete information on race (White, Black, Asian), intrinsic subtype (Luminal A/B, HER2-enriched, Basal-like), and survival status. Differential expression analysis was performed using DESeq2 to compare tumor samples from deceased versus living patients within each race×subtype group. Genes with |log2fold change|>1 and FDR<0.05 were considered differentially expressed (DEGs). All DEGs were used to apply the WGCNA algorithm in three stages: (1) construction of weighted gene correlation networks, (2) identification of coexpression modules, and (3) correlation of module eigengenes with clinical traits (race, subtype, and their interaction). Hub genes were defined as those with high module membership (MM>0.8) and gene significance (GS>0.2). We prioritized hub genes that were prevalent across multiple traits. Results: A total of 3548 DEGs were identified across intrinsic subtypes and race and finally 7 gene coexpression modules were recognized. The turquoise module was the largest (709 genes), followed by blue (538 genes) and brown (407 genes). However, the last one exhibited the strongest inverse correlation with basal-like tumors across racial groups, including both White (R=-0.66, p<0.001) and Black (R=-0.47, p<0.001) patients. In contrast, it showed a positive association with luminal A tumors, particularly among White patients (R=0.57, p<0.001). In this module, several hub genes demonstrated consistent associations across intrinsic subtypes and racial groups. Notably, C6orf97 and ESR1 showed high MM and GS in more than 50% of the evaluated traits, including basal-like, HER2-enriched, luminal A/B subtypes, and race-specific combinations (Basal-Black, Luminal-White). Additional genes such as TBC1D9 (42%), AGR3 (37%), DNALI1 (37%), and GATA3 (37%) also showed strong connectivity and relevance across multiple subtypes and racial backgrounds. Conclusions: Using the WGCNA method we were able to identify 10 hub genes, notably including ESR1 and GATA3, which are strongly linked to breast cancer, nevertheless findings need to be validated in other cohorts to confirm their significance. Citation Format: E. D. Lee-Kay-Pen, Y. Ferreyra, X. Fernandez, Z. Morante, T. Vidaurre, H. L. Gomez. Identifying hub genes among different breast cancer subtypes and racial diversity through integrated bioinformatics analysis (WGCNA) [abstract]. In: Proceedings of the San Antonio Breast Cancer Symposium 2025; 2025 Dec 9-12; San Antonio, TX. Philadelphia (PA): AACR; Clin Cancer Res 2026;32(4 Suppl):Abstract nr PS4-05-17.