A novel gene-based model for prognosis prediction of head and neck squamous cell carcinoma

Abstract

Abstract Background Head and neck squamous cell carcinoma (HNSCC) is a significant global health challenge. The identification of reliable prognostic biomarkers and construction of an ac-curate prognostic model are crucial. Methods In this study, mRNNA expression data and clinical data of HNSCC patients from The Cancer Genome Atlas (TCGA) were used. Overlapping candidate genes (OCGs) were identified by intersecting differentially expressed genes (DEGs)and prognosis-related genes. Best prognostic genes were selected using LASSO-COX regression based on OCGs, and a risk score was developed using the Cox coefficient of each gene. The prognostic power of the risk score was assessed using Kaplan-Meier survival analysis and time-dependent ROC analysis. Univariate and multivariate Cox regression were performed to identify independent prognostic parameters, which were used to construct a nomogram. The predictive accuracy of the nomogram was evaluated using calibration plots. Functional enrichment analysis of risk score related genes was performed to explore the potential biological pathways. External validation was conducted using data from the Gene Expression Omnibus (GEO) and ArrayExpress databases. Results FADS3, TNFRSF12A, TJP3, and FUT6 were screened to be significantly related to prognosis in HNSCC patients. The risk score effectively stratified patients into high-risk group with poor overall survival (OS) and low-risk group with better OS. Risk score, age, clinical M stage, clinical N stage were regarded as independent prognostic parameters by Cox regression analysis and used to construct a nomogram. The nomogram performed well in 1-, 2-, 3-, 5- and 10-year survival predictions. Functional enrichment analysis suggested that tight junction was closely related to the cancer. In addition, the prognostic power of the risk score was validated by external data sets. Conclusions This study constructed a gene-based model integrating clinical prognostic parameters to accurately predict prognosis in HNSCC patients.