Integrated Multi-Omics Analysis for the Identification of Disease-Associated Variations and Prognostic Biomarkers in Triple-Negative Breast Cancer (TNBC)
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Background
Triple-negative breast cancer (TNBC) exhibits high molecular heterogeneity. While multi-omic panels capture disease complexity, translating these profiles into actionable, cost-effective prognostic tools remains analytically challenging.
Objective
To mathematically distill a high-dimensional multi-omic profile into a lean, highly predictive biomarker panel. Furthermore, we aimed to construct, validate, and clinically anchor a prognostic survival nomogram.
Methods
Matched TCGA-BRCA transcriptomic and epigenomic data (n=5546) were integrated utilizing MOFA2. Functional pathways were mapped via the Enrichr database against Reactome, KEGG, and WikiPathways libraries. A machine learning ensemble (LightGBM, Random Forest) optimized the discovery signature. Prognostic stability was validated via Kaplan-Meier stratification, continuous Z-score Multivariate Cox Regression, and Time-Dependent ROC modeling. The tumor microenvironment was profiled via ssGSEA, and immunotherapy checkpoint correlation was assessed. External validation was executed on a microarray cohort (GSE58812).
Results
A 47-gene discovery signature was computationally optimized into a 15-gene clinical panel (Internal AUC = 0.9898). Kaplan-Meier analysis demonstrated profound prognostic separation (p < 0.0001). Multivariate Cox regression confirmed the signature’s independent prognostic value (Hazard Ratio = 10.67, p < 0.001). Immune profiling revealed the signature is driven by tumor-intrinsic factors, showing no significant correlation with local checkpoint expressions like PD-L1 (p = 0.72). External validation achieved an integrated multi-covariate AUC of 0.6874.
Conclusion
This optimized 15-gene signature and the associated clinical-genomic nomogram provide an accurate, independent, and generalizable framework for individualized TNBC survival prediction.
