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<p>The immune system is crucial in the development and advancement of cancerous tumors, particularly in head and neck squamous cell carcinoma (HNSC). This study aimed to identify immune-related gene signatures (IRGs) for predicting the prognosis of HNSC. Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), in addition to immune gene data from ImmPort, were examined. Using Cox-LASSO screening, nine IRGs were identified, and patients were classified into high- and low-risk cohorts based on risk scores. Differential expression, survival analysis, Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), single-sample GSEA (ssGSEA), CIBERSORT, and drug sensitivity analyses were performed between the cohorts. The high-risk cohorts exhibited lower immune scores and survival rates, while the low-risk cohorts exhibited higher immune scores and better outcomes. Cox regression identified CD19, CD79A, CTLA4, ICOS, and LAT as protective genes and CHGB, DKK1, PDGFA, and PTX3 as risk genes. Based on these nine genes, we established a nomogram prediction model to further assess patient prognosis. These findings highlight the prognostic value of IRGs in HNSC, offering the potential for personalized treatment strategies based on immune risk profiles.</p>

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Immunogenomic signatures defining the molecular subtypes of head and neck cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月14日

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  • Liangbin Gao,
  • Bin Wang,
  • Jiamin Wang,
  • Yu Chen,
  • Ning Li,
  • Wei An,
  • Zhilin Li

摘要

The immune system is crucial in the development and advancement of cancerous tumors, particularly in head and neck squamous cell carcinoma (HNSC). This study aimed to identify immune-related gene signatures (IRGs) for predicting the prognosis of HNSC. Transcriptome data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), in addition to immune gene data from ImmPort, were examined. Using Cox-LASSO screening, nine IRGs were identified, and patients were classified into high- and low-risk cohorts based on risk scores. Differential expression, survival analysis, Gene Set Enrichment Analysis (GSEA), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), single-sample GSEA (ssGSEA), CIBERSORT, and drug sensitivity analyses were performed between the cohorts. The high-risk cohorts exhibited lower immune scores and survival rates, while the low-risk cohorts exhibited higher immune scores and better outcomes. Cox regression identified CD19, CD79A, CTLA4, ICOS, and LAT as protective genes and CHGB, DKK1, PDGFA, and PTX3 as risk genes. Based on these nine genes, we established a nomogram prediction model to further assess patient prognosis. These findings highlight the prognostic value of IRGs in HNSC, offering the potential for personalized treatment strategies based on immune risk profiles.