Bioinformatics analysis for immune hub genes in BLIS subtype of triple-negative breast cancer

Abstract Background Triple-negative breast cancer (TNBC) characterizes a significant clinical challenge due to limited therapeutic options resulting from the nonexistence of hormone receptors and HER2. The Basal-Like Immune-Suppressed (BLIS) subtype exhibits intensely poor outcomes due to immune avoidance mechanisms. This study employs inclusive bioinformatics approaches to recognize immune-related hub genes within the BLIS subtype to uncover potential therapeutic targets and prognostic biomarkers. Starting with the gene expression dataset containing 58,000 genes from 360 TNBC patients, we filtered low-expression genes and applied variance stabilizing transformation (VST) using DESeq2. Differential expression analysis across the four recognized TNBC subtypes—BLIS, Mesenchymal (MES), Luminal Androgen Receptor (LAR), and Immunomodulatory (IM) identified 353 significantly expressed genes, comprising 124 upregulated and 229 downregulated differentially expressed genes (DEGs). Pathway enrichment analysis revealed significant dysregulation of immune-related processes. We constructed a protein–protein interaction (PPI) network with 36 genes and applied Density-Based Spatial Clustering of Applications with Noise (DBSCAN) in STRING with a high confidence threshold (0.900). Using Cytoscape based on the Matthews Correlation Coefficient (MCC) method, we identified ten hub genes with the highest network connectivity: CXCR3, CXCL10, IFNG, CCL5, CXCL9, CCR5, CX3CL1, CCL11, CCL4, and CXCL11 . Results Focusing on downregulated immune-related hub genes in the BLIS subtype, Kaplan–Meier survival analysis based on relapse-free survival (RFS) and subsequent multivariate analysis identified CCR5 and IFNG as novel biomarkers significantly associated with survival outcomes. Conclusions Our findings provide a foundation for developing immune-targeted therapeutic approaches for BLIS-TNBC patients and provision the integration of machine learning models to predict treatment responses and optimize patient-specific strategies, potentially transforming the clinical management of this challenging breast cancer subtype.