Establishment and validation of a prognostic model for oral squamous cell carcinoma based on ribosomal and apoptosis-related gene expression.

BACKGROUND: Oral squamous cell carcinoma (OSCC) is a major cause of cancer-related mortality worldwide. This study aimed to identify differentially expressed ribosomal and apoptosis-related genes (RARGs) in OSCC and develop a gene-based prognostic model to improve patient stratification and outcome predictions. METHODS: Gene expression profiles were analyzed from The Cancer Genome Atlas (TCGA) OSCC cohort (328 OSCC samples and 32 control samples) and Gene Expression Omnibus datasets (GEO, GSE2355827: 27 OSCC samples and 5 control samples, and GSE25099: 57 OSCC samples and 22 control samples). A total of 849 RARGs were obtained from GeneCards and PubMed databases. Overlaps between differentially expressed genes (DEGs) and RARGs were identified and prognostic genes were selected using Cox regression analysis. These data were used to construct a risk model. The prognostic performance was validated using time-dependent receiver operating characteristic curves and Kaplan–Meier survival analysis. Key gene expression was validated by qRT-PCR. RESULTS: We identified 3,513 DEGs in the TCGA-OSCC dataset, including 1,721 upregulated and 1,792 downregulated genes. Thirty-two ribosomal and apoptosis-related DEGs were identified. Survival analysis demonstrated significant differences in overall survival rates between the high- and low-risk groups. Six genes (SLC25A4, PLAU, IGF2BP2, CDKN2A, CCL26, and ALB) were incorporated into the final model, which achieved ROC-AUC scores above 0.6 for 1-, 2-, and 3-year survival predictions. qRT-PCR was performed to validate the expression patterns of Six genes. CONCLUSION: This study revealed the potential mechanism link between abnormal ribosome production and unbalanced apoptosis in OSCC. Based on this assumption, a prognostic model for RARGs expression characteristics was constructed. This model integrates the intrinsic molecular characteristics of tumors with systemic factors such as host nutrition and inflammation, enhances biological interpretation, and provides new ideas for patient individualized prognosis assessment and treatment decisions. Its stability and clinical practicality need to be further verified in a larger, multi-center population in the future.