Construction and validation of a prognostic model based on immune-metabolic-related genes in oral squamous cell carcinoma

Abstract

Oral squamous cell carcinoma (OSCC), a significant type of head and neck cancer, has witnessed increasing incidence and mortality rates. Immune-related genes (IRGs) and metabolic-related genes (MRGs) play essential roles in the pathogenesis, metastasis, and progression of OSCC. This study exploited data from The Cancer Genome Atlas (TCGA) to identify IRGs and MRGs related to OSCC through differential analysis. Univariate Cox analysis was utilized to determine immune-metabolic-related genes (IMRGs) associated with patient prognosis. A prognostic model for OSCC was constructed using Lasso-Cox regression and subsequently validated with datasets from the Gene Expression Omnibus (GEO). Non-Negative Matrix Factorization (NMF) clustering identified three molecular subtypes of OSCC, among which the C2 subtype showed better overall survival (OS) and progression-free survival (PFS). A prognostic model based on nine IMRGs was developed to categorize OSCC patients into high- and low-risk groups, with the low-risk group demonstrating significantly longer OS in both training and testing cohorts. The model showed strong predictive capabilities, and the risk score served as an independent prognostic factor. Additionally, expression levels of programmed death 1 (PD1) and cytotoxic T-lymphocyte-associated antigen 4 (CTLA4) differed between the risk groups. Gene Set Enrichment Analysis (GSEA) indicated distinct enriched pathways between high-risk and low-risk groups, highlighting the crucial roles of immune and metabolic processes in OSCC. The nine IMRGs prognostic model presented excellent predictive performance and has potential for clinical application.