A Risk Score Model Based on Drug-Sensitivity-Related Genes Has the Potential to Predict Oral Squamous Cell Carcinoma Prognosis.

Abstract

Objective: To develop a risk score model based on drug-sensitivity-related genes to predict the prognosis of patients with oral squamous cell carcinoma (OSCC).

Methods and materials: In this study, transcriptome from OSCC patients was downloaded from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) databases, and differential gene expression analysis was performed using R's 'limma' package. LASSO Cox regression identified key prognostic genes. We stratified patients into low- and high-risk groups and estimated survival rates using Kaplan-Meier. Gene set enrichment analysis (GSEA) and immune infiltration analysis were conducted to understand the potential pathways and tumour microenvironment. A nomogram model was constructed for prognosis prediction.

Results: Our study identified 118 candidate genes from three data sets and narrowed them down to four prognostic genes (IGF2BP2, PLAU, CEP55, CMYA5) using univariate Cox regression and LASSO Cox regression. A risk score model was developed which could predict patient prognosis. The model's prognostic value was independent of age, gender, and stage. A nomogram model incorporating risk score and age was constructed for personalised survival prediction. Tumour mutation burden analysis showed that the mutation rate of TP53 was higher in the high-risk group. Immune landscape analysis uncovered distinct immune cell infiltration patterns and immune checkpoint expression levels between different risk groups, suggesting implications for immunotherapy strategies.

Conclusion: The risk score model constructed using drug-sensitivity-related genes IGF2BP2, PLAU, CEP55, and CMYA5 may predict the prognosis of OSCC patients.