ArticleScientific reports2024
Characterization of macrophages in head and neck squamous cell carcinoma and development of MRG-based risk signature.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 9 citations in OpenAlex.
- A preliminary exploration of the role and mechanisms of CD93 in promoting the malignant progression of head and neck squamous cell carcinoma.Frontiers in pharmacology · 2026Article
- Integrin α5β1 in head and neck squamous cell carcinoma: expression, mechanisms, and clinical implications.American journal of cancer research · 2026Review
- Understanding the Tumor Microenvironment and Therapy Resistance in Head and Neck Squamous Cell Carcinoma.Cells · 2025Review
- Characterization of PANoptosis-related expression pattern, prognosis and tumor microenvironment in head and neck squamous cell carcinoma.Discover oncology · 2025Article
- Immune Evasion in Head and Neck Squamous Cell Carcinoma: Roles of Cancer-Associated Fibroblasts, Immune Checkpoints, andCancers · 2025Review
- Advances and challenges in CAR-T cell therapy for head and neck squamous cell carcinoma.Biomarker research · 2025Review
- Exploring Renin-angiotensin System Genes as Novel Prognostic Biomarkers for Oral Squamous Cell Carcinoma.International journal of medical sciences · 2025Article
- Personalized Treatment Strategies via Integration of Gene Expression Biomarkers in Molecular Profiling of Laryngeal Cancer.Journal of personalized medicine · 2024Review
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Macrophages are immune cells in the TME that can not only inhibit angiogenesis, extracellular matrix remodeling, cancer cell proliferation, and metastasis but also mediate the phagocytosis and killing of cancer cells after activation, making them key targets in anti-tumor immunotherapy. However, there is little research on macrophages and their relation to disease prognosis in HNSCC. Initially, we collected scRNA-seq, bulk RNA-seq, and clinical data. Subsequently, we identified macrophages and distinguished MRGs. Using the K-means algorithm, we performed consensus unsupervised clustering. Next, we used ssGSEA analysis to assess immune cell infiltration in MRG clusters. A risk model was established using multivariate Cox analysis. Then, Kaplan-Meier, ROC curves, univariate and multivariate COX analyses, and C-index was used to validate the predictive power of the signature. The TIDE method was applied to assess the response to immunotherapy in patients diagnosed with HNSCC. In addition, drug susceptibility predictions were made for the GDSC database using the calcPhenotype function. We found that 8 MRGs had prognostic potential. Patients in the MRG group A had a higher probability of survival, and MRG clusters A and B had different characteristics. Cluster A had a higher degree of expression and infiltration in MRG, indicating a closer relationship with MRG. The accuracy of the signature was validated using univariate and multivariate Cox analysis, C-index, and nomogram. Immune landscape analysis found that various immune functions were highly expressed in the low-risk group, indicating an improved response to immunotherapy. Finally, drugs with high sensitivity to HNSCC (such as 5-Fluorouracil, Temozolomide, Carmustine, and EPZ5676) were explored and analyze the malignant characteristics of HNSCC. We constructed a prognostic model using multivariate Cox analysis, consisting of 8 MRGs (TGM2, STC1, SH2D3C, PIK3R3, MAP3K8, ITGA5, ARHGAP4, and AQP1). Patients in the low-risk group may have a higher response to immunotherapy. The more prominent drugs for drug selection are 5-fluorouracil, temozolomide and so on. Malignant features associated with HNSCC include angiogenesis, EMT, and the cell cycle. This study has opened up new prospects for the prognosis, prediction, and clinical treatment strategy of HNSCC.
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