Integrated analysis of methylation and transcriptome identifies a novel risk model for diagnosis, prognosis, and immune characteristics in head and neck squamous cell carcinoma.

BACKGROUND: DNA methylation is an important epigenetic modification that plays a crucial role in the development and progression of various tumors. However, the association between methylation­driven genes and diagnosis, prognosis, and immune characteristics of head and neck squamous cell carcinoma (HNSCC) remains unclear. METHODS: We obtained transcriptome, methylation, and clinical data from HNSCC patients in TCGA database, and used MethylMix algorithm to identify methylation-driven genes. A methylation driven gene-related risk model was constructed using Lasso regression analysis, and validated using data from GEO database. Immune infiltration and immune function analysis of the expression profiles were conducted using ssGSEA. Differences in immune checkpoint-related genes were analyzed, and the efficacy of immunotherapy was evaluated using TCIA database. Finally, a series of cell functional experiments were conducted to validate the results. RESULTS: Five methylation-driven genes were identified and utilized to construct a prognostic risk model. Based on the median risk score, all patients were categorized into high-risk and low-risk groups. The K-M analysis revealed that patients in the high-risk group have a worse prognosis. Additionally, the risk model demonstrated better prognostic predictive value as indicated by ROC analysis. GSEA enrichment analysis indicated that gene sets in the high and low-risk groups were primarily enriched in pathways associated with tumor immunity and metabolism. Our subsequent investigations showed that high-risk patients exhibited more immunosuppressive phenotypes, while low-risk patients were more likely to respond positively to immunotherapy. CONCLUSION: These findings of our research have the potential to improve patient stratification, guide treatment decisions, and advance the development of personalized therapies for HNSCC.

Identifying optimal candidates for primary tumor surgery in patients with metastatic head and neck cancer.

Background: Primary tumor surgery (PTS) may enhance survival among part of patients with metastatic head and neck cancer (mHNC). Herein, a predictive model was needed to construct to identify who can gain benefit remarkably from tumor resection. Methods: Data of patients with mHNC were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. The best cut-off value of age were analyzed using the X-tile software. One-to-one PSM, Kaplan-Meier method, and log-rank test were performed for survival analysis.The independent factors determined using the multivariate Cox proportional hazard regression were used to construct the nomogram. Results: A total of 1,614 patients diagnosed with mHNC were included; among them, 356 (22.0%) underwent a surgical procedure for the excision of the primary tumor. cancer-specific survival (CSS) was remarkably prolonged in the PTS group relative to the non-PTS group following PSM [Median:19 months vs. 9 months; hazard ratio (HR) 0.52, P < 0.001]. Patients with mHNC who were younger than 52 years old, had well-differentiated tumors, had T1 and N0 stages, and were married at the time of the study may have significantly benefited from PTS. In addition, we constructed a nomogram based on the factors that independently affect the CSS in multivariate Cox analysis. The nomogram showed excellent discrimination in both the training and validation sets (AUC: 0.732 and 0.738, respectively). Conclusion: A practical predictive model was constructed to determine the appropriate patients with mHNC, who would benefit from surgical resection.