Disulfidptosis features and prognosis in head and neck squamous cell carcinoma patients: unveiling and validating the prognostic signature across cohorts

Abstract

Abstract Background Head and neck squamous cell carcinoma (HNSCC) is a significant health concern with a variable global incidence and is linked to regional lifestyle factors and HPV infections. Despite treatment advances, patient prognosis remains variable, necessitating an understanding of its molecular mechanisms and the identification of reliable prognostic biomarkers. Methods We analyzed 959 HNSCC samples and employed batch correction to obtain consistent transcriptomic data across cohorts. We examined 79 disulfidptosis-related genes to determine consensus clusters and utilized high-throughput sequencing to identify genetic heterogeneity within tumors. We established a disulfidptosis prognostic signature (DSPS) using least absolute shrinkage and selection operator (LASSO) regression and developed a prognostic nomogram integrating the DSPS with clinical factors. Personalized chemotherapy prediction was performed using the "pRRophetic" R package. Results Batch corrections were used to harmonize gene expression data, revealing two distinct disulfidptosis subtypes, C1 and C2, with differential gene expression and survival outcomes. Subtype C1, characterized by increased expression of the MYH family genes ACTB, ACTN2, and FLNC, had a mortality rate of 48.4%, while subtype C2 had a mortality rate of 38.7% (HR = 0.77, 95% CI: 0.633–0.934, P = 0.008). LASSO regression identified 15 genes that composed the DSPS prognostic model, which independently predicted survival (HR = 2.055, 95% CI: 1.420–2.975, P < 0.001). The prognostic nomogram, which included the DSPS, age, and tumor stage, predicted survival with AUC values of 0.686, 0.704, and 0.789 at 3, 5, and 8 years, respectively, indicating strong predictive capability. In the external validation cohort (cohort B), the DSPS successfully identified patients at greater risk, with worse overall survival outcomes in the high-DSPS subgroup (HR = 1.54, 95% CI: 1.17–2.023, P = 0.002) and AUC values of 0.601, 0.644, 0.636, and 0.748 at 3, 5, 8, and 10 years, respectively, confirming the model's robustness. Conclusion The DSPS provides a robust prognostic tool for HNSCC, underscoring the complexity of this disease and the potential for tailored treatment strategies. This study highlights the importance of molecular signatures in oncology, offering a step toward personalized medicine and improved patient outcomes in HNSCC management.