AI-driven epigenomic profiling reveals early predictors of cutaneous squamous cell carcinoma risk in hidradenitis suppurativa

Background: Hidradenitis Suppurativa (HS) is a chronic inflammatory skin disease with a markedly increased risk of cutaneous squamous cell carcinoma (cSCC), including aggressive and often fatal variants . Although cSCC arises through ultraviolet injury, chronic inflammation, impaired wound repair, and smoking, its molecular drivers remain incompletely defined. Emerging evidence implicates epigenetic dysregulation, particularly DNA methylation, as an early indicator of carcinogenic potential. Our HS cohort contained no cSCC cases at recruitment, providing a unique opportunity to identify early epigenomic alterations preceding malignant transformation. Methods: Genome-wide DNA methylation was profiled in blood from 24 HS cases and matched controls using the Illumina MethylationEPIC array. Differentially methylated CpGs were identified through a rigorous bioinformatic pipeline and cross-referenced with cSCC datasets to define overlap with oncogenic pathways. Artificial intelligence approaches, including deep learning, Cox elastic-net survival modeling, random forest, and integrated AI/ML pipelines, were used to prioritize CpGs associated with cSCC risk. Results: We identified significant methylation alterations (FDR ≤ 0.05) at 32 CpG sites across 32 genes in HS, comprising 24 hypomethylated and 8 hypermethylated loci. All are previously implicated in cSCC and converge on canonical oncogenic pathways, including EGFR/MAPK, p53, TERT, NOTCH signaling, and DNA repair/chromatin remodeling. The cSCC tumor-trained Cox proportional hazards model (Cox model) demonstrated strong prognostic performance (C-index 0.78-0.84 across cross-validation). Applying this model to HS samples generated a continuous cSCC Epigenetic Prognostic Score (cSCC-EPS) that stratified HS patients into low-, intermediate-, and high-risk methylation phenotypes. SHapley Additive exPlanations (SHAP) analyses highlighted CpGs within EGFR, DNMT1, TP53, NOTCH3, BRAF, and TERT as the strongest contributors to cSCC predicted risk. This integrative AI-ML epigenetic pipeline identified HS patients with methylation profiles converging on high-risk cSCC tumor biology, suggesting a blood-based molecular window into early carcinogenic processes. Conclusions: HS patients harbor blood-based methylation signatures that mirror early epigenomic alterations seen in cSCC. This AI-integrated framework provides the first evidence that these biomarkers can predict malignant transformation in HS, positioning methylation profiling as a promising tool for early cSCC risk assessment.