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

• Published in: Molecular genetics and genomics : MGG Vol. 299; no. 1; p. 71
• Main Authors: Zhang, Jun-wei, Gao, Xi-Lin, Li, Sheng, Zhuang, Shuang-hao, Liang, Qi-Wei
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.12.2024; Springer Nature B.V
• Subjects: Animal Genetics and Genomics; Biochemistry; Biomedical and Life Sciences; Diagnosis; DNA methylation; Epigenetics; Genes; Head & neck cancer; Head and neck carcinoma; Human Genetics; Immune checkpoint; Immune response; Immunotherapy; Life Sciences; Metastases; Microbial Genetics and Genomics; Original Article; Patients; Phenotypes; Plant Genetics and Genomics; Prognosis; Risk groups; Squamous cell carcinoma; Transcriptomes; Prognosis; Methylation; HNSCC; Immunotherapy; Transcriptome
• ISSN: 1617-4615; 1617-4623; 1617-4623
• DOI: 10.1007/s00438-024-02164-z

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 methylation‑driven 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.