Comprehensive analysis of immune cell infiltration and significant genes in head and neck squamous cell carcinoma

Objective: Immunotherapy directed at the tumor microenvironment is effective in the treatment of head and neck squamous cell carcinoma (HNSCC). In contrast, there has been a paucity of research on the relationship between the HNSCC microenvironment and prognostic outcome. Meanwhile, tumor immune cell infiltration (ICI) has emerged as a critical step in immunotherapy.Methods: Two algorithms, CIBERSORT and ESTIMATE, were performed to evaluate the ICI view of 885 HNSCC patients using three databases: the Cancer Genome Atlas (TCGA), Arrayexpress, and Gene Expression Omnibus (GEO).Results: Different ICI subtypes were identified. Following that, 57 different expression genes (DEGs) were discovered. The ICI scores of all patients were calculated using the Principal Component Analysis (PCA) algo-rithm. Additionally, an immune-related prognostic signature was developed and validated using 17 of 57 DEGs. Patients with a low-ICI or low-risk score had a higher infiltration immune-activated related cells and higher expression of most immune checkpoint-related molecules, indicating a better prognosis. Furthermore, using the pRRophetic algorithm, the sensitivities of many chemotherapeutic drugs were significantly different between two ICI subtypes or two risk groups. Moreover, a nomogram incorporating the ICI score, risk score, and clinical characteristics was developed and was capable of accurately predicting outcomes. Conclusion: The ICI score and 17-gene signature could improve HNSCC survival prediction, promote individual treatment strategies, and provide promising novel immunotherapy biomarkers.

Automated summary

Immunotherapy targeting the tumor microenvironment is effective in treating HNSCC, but there has been limited research on the relationship between HNSCC microenvironment and prognostic outcome. This study evaluated tumor immune cell infiltration and developed an immune-related prognostic signature and prediction model, providing promising clues for improving HNSCC survival prediction, individualized treatment strategies, and discovering immunotherapy biomarkers.