AI-driven multi-omics integration of cancer-associated fibroblasts for prognostic modeling and therapeutic target discovery in head and neck squamous cell carcinoma.

The Head and Neck Squamous Cell Carcinoma (HNSCC), arising from the mucosal epithelium of the oral cavity, pharynx, and larynx, continues to represent a major worldwide health burden due to its high mortality rates and late-stage diagnosis. A contribution of this study is the focus on the heterogeneity of CAFs, which directly impacts therapeutic response and resistance. To address this, we applied an AI-driven, multi-omics integration strategy to elucidate CAF-mediated mechanisms in HNSCC progression and therapy. Bulk transcriptomic data from Gene Expression Omnibus (GEO) were intersected with curated CAF gene sets to identify CAF-related differentially expressed genes (CAFs-DEGs). To create a fibroblast-associated prognosis signature, a machine learning-based LASSO-Cox regression model has been developed using the TCGA-HNSCC cohort. Prognostic performance was validated through Kaplan-Meier survival analysis, time-dependent ROC, nomogram, Decision Curve Analysis (DCA), and calibration curves. To provide mechanistic insights, immune infiltration profiling, checkpoint correlations, single-cell expression mapping, tumor mutational burden (TMB), microsatellite instability (MSI), and DNA methylation analyses were performed. Furthermore, therapeutic vulnerabilities were explored by integrating drug sensitivity prediction, AI-assisted cMAP screening, and molecular docking validation, which identified Epothilone B as a promising agent targeting HBEGF. Overall, this research shows that understanding the heterogeneity of CAFs with AI-enabled multi-omics modeling can reveal prognostic biomarkers and therapeutic targets for overcoming resistance, with the ultimate goal of improving precision oncology for HNSCC.