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

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.

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.

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.

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.