Disulfidptosis in head and neck squamous carcinoma: Integrative bioinformatic and in‐vitro analysis.

Background: Head and neck squamous carcinoma (HNSC) is a prevalent global malignancy with limited treatment options, which necessitates the development of novel therapeutic strategies. Disulfidptosis, a recently discovered and unique cell death pathway, may offer promise as a treatment target in HNSC. Materials and Methods: We identified disulfidptosis‐related genes (DRGs) using multiple algorithms and developed a prognostic model based on a disulfidptosis‐related gene index (DRGI). The model's predictive accuracy was assessed by ROC‐AUC, and patients were stratified by risk scores. We investigated the tumor immune microenvironment, immune responses, tumorigenesis pathways, and chemotherapy sensitivity (IC50). We also constructed a diagnostic model using 20 machine‐learning algorithms and validated PCBP2 expression through RT‐qPCR and western blot. Results: We developed a 12‐DRG DRGI prognostic model, classifying patients into high‐ and low‐risk groups, with the high‐risk group experiencing poorer clinical outcomes. Notable differences in tumor immune microenvironment and chemosensitivity were observed, with reduced immune activity and suboptimal treatment responses in the high‐risk group. Advanced machine learning and in‐vitro experiments supported DRGI's potential as a reliable HNSC diagnostic biomarker. Conclusion: We established a novel DRGI‐based prognostic and diagnostic model for HNSC, exploring its tumor immune microenvironment implications, and offering valuable insights for future research and clinical trials. [ABSTRACT FROM AUTHOR]