ABSTRACT The cellular and molecular complexity of acne pathogenesis has hindered progress toward effective targeted therapies. While keratinocytes are known to influence skin inflammation, their precise transcriptional programs and regulatory circuitry in acne remain unclear. We developed an integrative computational framework that combines single‐cell RNA sequencing (scRNA‐seq), gene co‐expression network analysis (WGCNA), and two complementary machine learning algorithms (SVM‐RFE, LASSO) to identify disease‐relevant biomarkers. We mapped acne lesion cellular composition, reconstructed keratinocyte differentiation trajectories, and integrated miRNA–transcription factor–drug interaction networks to link molecular signatures to potential interventions. We uncovered marked keratinocyte heterogeneity and enriched late‐stage pro‐inflammatory states in acne lesions, accompanied by increased macrophage/monocyte and T cell infiltration. Six keratinocyte‐associated biomarkers (PYGL, C10orf99, C12orf75, S100A2, PI3, CARD18) were identified, achieving high diagnostic accuracy (AUC > 0.85). Functional enrichment connected these genes to cytokine and chemokine signaling, while regulatory analysis revealed upstream modulators (hsa‐let‐7b‐5p, FOXC1). Drug–gene network mapping suggested repurposing potential for cyclosporin A and valproic acid. In conclusion, our study delineates a keratinocyte‐centered molecular signature that shapes acne pathogenesis and provides potential therapeutic biomarkers.
Cai et al. (2026) studied this question.