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Heterogeneity analysis and prognostic model construction of HPV negative oral squamous cell carcinoma T cells using ScRNA-seq and bulk-RNA analysis

Funct Integr Genomics. 2025 Jan 23;25(1):25. doi: 10.1007/s10142-024-01525-6.

ABSTRACT

BACKGROUND: T cells are involved in every stage of tumor development and significantly influence the tumor microenvironment (TME). Our objective was to assess T-cell marker gene expression profiles, develop a predictive risk model for human papilloma virus (HPV)-negative oral squamous cell carcinoma (OSCC) utilizing these genes, and examine the correlation between the risk score and the immunotherapy response.

METHODS: We acquired scRNA-seq data for HPV-negative OSCC from the GEO datasets. We performed cell‒cell communication, trajectory, and pathway enrichment analyses of T-cell-associated genes. In addition, we constructed and validated a T-cell-associated gene prognostic model for HPV-negative OSCC patients using TCGA and GEO data and assessed the immune infiltration status of HPV-negative OSCC patients .qRT-PCR was used to detect the expression level of prognosis-related genes in different risk groups.

RESULTS: ScRNA-seq was conducted on 28,000 cells derived from 14 HPV-negative OSCC samples and 6 normal samples. We identified 4,635 T cells from these cells and identified 774 differentially expressed genes(DEGs) associated with T cells across five distinct T-cell subtypes. Through the integration of bulk-RNAseq data, we established a prognostic model based on DEGs related to T cells. By separating patients into high-risk and low-risk groups according to these prognostic related genes, we can accurately predict their survival rates and the immune infiltration status of the TME.qRT-PCR results showed that compared with the patients of low risk group, the expression of PMEPA1, SH2D2A, SMS and PRDX4 were significantly up-regulated in high risk group.

CONCLUSION: This study provides a resource for understanding the heterogeneity of T cells in HPV-negative OSCC patients and associated prognostic risk models. It provides new insights for predicting survival and level of immune infiltration in patients with HPV-negative OSCC.

PMID:39849233 | DOI:10.1007/s10142-024-01525-6

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