Identification of programmed cell death-related genes and construction of a prognostic model in oral squamous cell carcinoma using single-cell and transcriptome analysis.

BACKGROUND

Oral squamous cell carcinoma (OSCC) is characterized by poor prognosis and high mortality. Understanding programmed cell death-related genes could provide valuable insights into disease progression and treatment strategies.

METHODS

RNA-sequencing data from 341 OSCC tumor tissues and 31 healthy samples were analyzed from TCGA database, with validation using 76 samples from GSE41613. Single-cell RNA sequencing data was obtained from GSE172577 (6 OSCC samples). Differentially expressed genes (DEGs) were identified and intersected with 1,254 programmed cell death-related genes. A protein-protein interaction network was constructed, and key modules were identified. Univariate Cox, LASSO, and multivariate Cox regression analyses were performed to build a prognostic model. Model performance was evaluated using Kaplan-Meier analysis, ROC curves, and nomogram validation.

RESULTS

The study identified 200 candidate genes from the intersection of DEGs and programmed cell death-related genes, which were further refined to 57 hub genes through PPI network analysis. A prognostic signature consisting of five genes (MET, GSDMB, KIT, PRKAG3, and CDKN2A) was established and validated. The model demonstrated good predictive performance in both training and validation cohorts (AUC > 0.6 for 1-, 2-, and 3-year survival). Single-cell analysis revealed that prognostic genes were predominantly expressed in stromal and epithelial cells. Cell communication analysis indicated strong interactions between stromal and epithelial cells.

CONCLUSIONS

This study developed and validated a novel five-gene prognostic signature for OSCC based on programmed cell death-related genes. The model shows promising clinical application potential for risk stratification and personalized treatment of OSCC patients.