ArticleMolecular genetics and genomics : MGG2024
Integrated analysis of methylation and transcriptome identifies a novel risk model for diagnosis, prognosis, and immune characteristics in head and neck squamous cell carcinoma.
Article in Molecular genetics and genomics : MGG, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Machine learning-based prognostic model of stemness and angiogenesis-related genes for predicting prognosis and immune infiltration in patients with HCC.Scientific reports · 2026Article
- DNA methylation analysis of the epigenome in oral squamous cell carcinoma.Human genomics · 2026Article
- The Integrated Transcriptome Bioinformatics Analysis of Energy Metabolism-Related Profiles for Dorsal Root Ganglion of Neuropathic Pain.Molecular neurobiology · 2025Article
- Multi-omics in immunotherapy research for HNSCC: present situation and future perspectives.NPJ precision oncology · 2025Review
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5 authors.
Funding
Abstract
backgroundDNA 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.
methodsWe 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.
resultsFive 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.
conclusionThese findings of our research have the potential to improve patient stratification, guide treatment decisions, and advance the development of personalized therapies for HNSCC.
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