CD8 + T cell related prognostic risk model and tumor immune environment modulation in HNSCC based on single-cell and bulk RNA sequencing

Background CD8 ⁺ T cells have a crucial impact at different stages of tumor development and within the tumor microenvironment. The present study sought to develop a prognostic risk model based on the expression profile of CD8 ⁺ T cell associated genes in head and neck squamous cell carcinoma (HNSCC) and to explore the relationship between this risk model and the response to immunotherapy. Methods In our study, we retrieved scRNA-seq data from the Gene Expression Omnibus database (GEO) to identify differentially expressed CD8 ⁺ T cell associated that are unique to HNSCC. This dataset comprised 5902 cells from 18 patients with HNSCC. With the integration of the TCGA database, a prognostic risk model was developed based on the identified CD8 ⁺ T cell associated genes. We further extensively examined the performance of the prognostic risk model and devised a nomogram. In addition to evaluating the performance of the prognostic risk model, a correlation analysis of the immune microenvironment was conducted to investigate its associations with immune cell infiltration, immune checkpoint genes, and tumor mutation burden. Moreover, gene set enrichment analysis (GSEA) was employed to explore potential biological pathways related to these findings. Results In this study, scRNA-seq data were utilized to identify 582 CD8 ⁺ T cell genes that exhibited differential expression. Combining these findings with the TCGA dataset, a prognostic risk model consisting of 6 gene features was constructed. Subsequently, patients were categorized into high-risk and low-risk groups based on their respective risk scores. The predictive capabilities of the CD8 ⁺ T related risk model were demonstrated collectively by kaplan‒meier curves, ROC curves, multivariate cox analysis, and decision curve analysis curves. Furthermore, there were significant differences between the high-risk and low-risk groups in terms of immune cell infiltration levels, immune checkpoint genes, HLA-related genes, and immune-related pathways. Conclusion CD8 + T cell associated risk score can effectively screen patients at high risk of HNSCC and predict the prognosis of patients. Moreover, we observed significant associations between the prognostic model and immune cell infiltration, immune checkpoint genes, and immune-related pathways. These findings may pave the way for future research in the field of personalized treatment for HNSCC.