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<p>Head and neck squamous cell carcinoma (HNSCC) is a major health concern with considerable morbidity and mortality worldwide. Previous studies have applied single-cell sequencing to characterize the tumor microenvironment in HNSCC, providing insights into immune cell composition, stromal interactions, and malignant cell states. However, there remains a gap in analyzing lymph node metastasis (LM) and normal lymph node tissues (LN) by using single-cell RNA sequencing (scRNA-seq) analysis. We performed comparative scRNA-seq analysis on seven lymph node metastatic tissues of HNSCC patients and five non-metastatic tissues. We identified several cell types with significantly altered expression levels, such as CD8+ Tex cells, Macrophages, and Cancer-associated fibroblasts (CAFs). Tumor cells were classified into seven clusters, with cluster2 strongly linked to tumorigenesis and metastasis. Specific subsets such as CD4_Tfh_CXCL13, CD4_Treg_RPL26, CD8_teff_CREM, NK_GZMB, Macrophages_OLFML3 and macrophages_SPP1 cells were also associated with HNSCC progression and metastasis. Based on these findings, we constructed and validated a prognostic model for HNSCC using the expression of INHBA, SFRP2, SPP1, and IFI27. This model provides a tool for risk stratification and informs potential therapeutic strategies for HNSCC.</p>

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Single-cell transcriptomics reveals heterogeneity and immune microenvironment in lymphatic metastasis of head and neck squamous cell carcinoma

  • 期刊论文

  • 发表日期：

    2026年3月16日

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  • Guannan Wei,
  • Guanghao Zhang,
  • Shan Jiang,
  • Ce Li

摘要

Head and neck squamous cell carcinoma (HNSCC) is a major health concern with considerable morbidity and mortality worldwide. Previous studies have applied single-cell sequencing to characterize the tumor microenvironment in HNSCC, providing insights into immune cell composition, stromal interactions, and malignant cell states. However, there remains a gap in analyzing lymph node metastasis (LM) and normal lymph node tissues (LN) by using single-cell RNA sequencing (scRNA-seq) analysis. We performed comparative scRNA-seq analysis on seven lymph node metastatic tissues of HNSCC patients and five non-metastatic tissues. We identified several cell types with significantly altered expression levels, such as CD8+ Tex cells, Macrophages, and Cancer-associated fibroblasts (CAFs). Tumor cells were classified into seven clusters, with cluster2 strongly linked to tumorigenesis and metastasis. Specific subsets such as CD4_Tfh_CXCL13, CD4_Treg_RPL26, CD8_teff_CREM, NK_GZMB, Macrophages_OLFML3 and macrophages_SPP1 cells were also associated with HNSCC progression and metastasis. Based on these findings, we constructed and validated a prognostic model for HNSCC using the expression of INHBA, SFRP2, SPP1, and IFI27. This model provides a tool for risk stratification and informs potential therapeutic strategies for HNSCC.