Transcriptional and Pathway Level Heterogeneity in Luminal A Breast Cancer: A Framework for Precision Therapy.

BACKGROUND

Luminal A breast cancer is the most prevalent hormone receptor-positive subtype, traditionally associated with favorable prognosis due to its low proliferative nature. However, emerging evidence highlights substantial molecular heterogeneity that complicates prognosis and therapeutic decision-making. A detailed understanding of this heterogeneity is essential to guide precision oncology strategies.

METHODS

We applied a systems biology approach integrating transcriptomic analysis, pathway enrichment, protein-protein interaction networks (PPIN), weighted gene co-expression network analysis (WGCNA), transcription factor (TF) profiling, and drug-target interaction mapping. RNA-seq data from GSE233242 were analyzed using DESeq2 for differential expression. Enrichment analyses were performed using GSEA, KEGG, and EnrichR. Key TFs were identified through TRRUST and ChEA3 databases. Prognostic significance was evaluated using Kaplan-Meier Plotter.

RESULTS

We identified 1,999 differentially expressed genes (DEGs) in Luminal A tumors, with enrichment in cell cycle regulation, chromatin remodeling, and immune-related pathways, alongside suppression of metabolic processes. PPIN and WGCNA analyses revealed key hub genes and functional modules. TFs such as CEBPA, PPARG, and SOX2 emerged as master regulators, with several (e.g., SOX2, TLX1, PTTG1) associated with poor survival. Integration with drug-target databases identified potential repositioning candidates (e.g., dexamethasone, simvastatin). Notably, dysregulation in senescence, proteoglycan signaling, and ROS pathways suggested novel vulnerabilities in Luminal A tumors.

CONCLUSIONS

Our integrative multi-omics analysis reveals the complex molecular architecture of Luminal A breast cancer, characterized by transcriptional and pathway-level heterogeneity. The findings advocate for subtype-specific therapeutic interventions targeting transcription factor networks, redox balance, and the tumor microenvironment. This systems-level framework provides a foundation for precision risk stratification and treatment optimization in Luminal A breast cancer.