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<p>Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous cancer with significant global incidence. This study investigates glycolysis- and lactate metabolism-related genes (GALMRGs) in HNSCC, focusing on their impact on prognosis, the tumor immune microenvironment, and their potential as therapeutic biomarkers. Analysis of data from the Cancer Genome Atlas and Gene Expression Omnibus identified 16 GALMRGs that were differentially expressed in HNSCC compared to normal tissues. Functional analysis revealed the involvement of lactate and pyruvate metabolism and HIF-1 signaling pathways. Weighted gene co-expression network analysis identified two module genes, <i>CDKN3</i> and <i>SLC2A1</i>. Five key genes (<i>CAV1</i>, <i>CDKN3</i>, <i>LDHA</i>, <i>MB</i>, and <i>PER2</i>) were identified through univariate, multivariate, and LASSO regression analyses and used to construct a prognostic model. This model demonstrated strong predictive accuracy for overall survival, stratifying patients into high- and low-risk groups. Immune cell infiltration analysis showed a negative correlation between resting and activated mast cells, and low-risk patients had higher tumor mutational burden, suggesting a better response to immunotherapy. Consensus clustering classified HNSCC into two distinct molecular subtypes with differing expression of the key genes. This GALMRG-based prognostic model is a promising biomarker for predicting HNSCC outcomes and immunotherapy responses, providing valuable insights for personalized treatment strategies.</p>

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Bioinformatic analysis of glycolysis and lactate metabolism genes in head and neck squamous cell carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月28日

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  • Huanyu Jiang,
  • Lijuan Zhou,
  • Haidong Zhang,
  • Zhenkun Yu

摘要

Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous cancer with significant global incidence. This study investigates glycolysis- and lactate metabolism-related genes (GALMRGs) in HNSCC, focusing on their impact on prognosis, the tumor immune microenvironment, and their potential as therapeutic biomarkers. Analysis of data from the Cancer Genome Atlas and Gene Expression Omnibus identified 16 GALMRGs that were differentially expressed in HNSCC compared to normal tissues. Functional analysis revealed the involvement of lactate and pyruvate metabolism and HIF-1 signaling pathways. Weighted gene co-expression network analysis identified two module genes, CDKN3 and SLC2A1. Five key genes (CAV1, CDKN3, LDHA, MB, and PER2) were identified through univariate, multivariate, and LASSO regression analyses and used to construct a prognostic model. This model demonstrated strong predictive accuracy for overall survival, stratifying patients into high- and low-risk groups. Immune cell infiltration analysis showed a negative correlation between resting and activated mast cells, and low-risk patients had higher tumor mutational burden, suggesting a better response to immunotherapy. Consensus clustering classified HNSCC into two distinct molecular subtypes with differing expression of the key genes. This GALMRG-based prognostic model is a promising biomarker for predicting HNSCC outcomes and immunotherapy responses, providing valuable insights for personalized treatment strategies.