Population-specific MicroRNA biomarker discovery in breast ductal carcinoma via explainable graph neural multi-omics modeling

Abstract

Background: Ductal carcinoma, including ductal carcinoma in situ (DCIS) and Invasive ductal carcinoma (IDC), represents a major global health burden, yet South Asian populations remain markedly under-represented in molecular oncology research. MicroRNAs (miRNAs) play critical roles in tumor progression, immune regulation, and therapy response; however, their population-specific relevance remains unclear. This study characterizes miRNA dysregulation in South Asian breast ductal carcinoma and evaluates their diagnostic, prognostic, and therapeutic potential using multi-omics integration, explainable machine learning, and functional validation. Methods: Tumor and matched adjacent-normal tissues from clinically confirmed ductal carcinoma patients (n = 800; 500 IDC, 300 DCIS) underwent miRNA-sequencing and clinical annotation. Differential expression, survival modeling, and pathway enrichment analyzes were performed. A Graph Attention Network (GAT) classifier with SHAP-based interpretability was developed for subtype prediction and treatment-response stratification. External validation was performed using the TCGA-BRCA dataset. Anti-miR-21 lipid nanoparticle (LNP) inhibition assays were conducted for functional assessment. Results: miR-21, miR-155, and miR-200b showed significant dysregulation in discovery cohort analysis, with diagnostic performance ranging from AUC 0.78–0.92. The GAT model achieved AUC = 0.96 (95% CI: 0.93–0.98), outperforming Random Forest (AUC = 0.94), and SHAP analysis highlighted miR-21 and miR-155 as the dominant contributors. Proof-of-concept anti-miR-21 LNP assays demonstrated 92% encapsulation efficiency, IC₅₀ = 21.4 nM, and ~ 45% tumor volume reduction in preclinical murine models. Conclusions: This study presents the first large-scale characterization of miRNA dysregulation in South Asian breast ductal carcinoma and reveals distinct population-specific prognostic behavior, particularly for miR-155. The combined genomic profiling, explainable GNN-based prediction, and preclinical therapeutic validation support further investigation of miRNA biomarkers for clinical translation. Findings support development of region-tailored liquid biopsy panels and precision therapy strategies for South Asian breast cancer patients.