A model of seven immune checkpoint-related genes predicting overall survival for head and neck squamous cell carcinoma

Abstract

Background Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease characterized by different molecular subtypes with different prognosis and response to treatment. Therefore, the aim of this study was to construct reliable gene signatures based on immune checkpoint-related genes to distinguish between subgroups of patients with different risks. Methods We obtained the HNSCC data from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) as a training set and the external validation set, respectively. First, differentially expressed immune checkpoint-related genes in tumor tissues and normal tissues were determined, and the potential functions of differential genes were explored through GO function annotation and KEGG pathway enrichment analysis. Using univariate Cox regression analysis, 20 immune checkpoint-related genes in HNSCC patients were significantly associated with overall survival (OS). Subsequently, seven genes were selected by multivariate Cox regression analysis to create a gene signature. Next, the stability of gene signatures was assessed using Kaplan-Meier curve, Time-dependent receiver operating characteristic (ROC) curve. Finally, we constructed a nomogram visualization modelled to facilitate subsequent clinical applications. Results A total of 80 differentially expressed genes (DEGs) were obtained, the GO analysis of these DEGs indicated that they were significantly enriched in positive regulation of cell activation, T cell activation; the KEGG analysis results performed and showed that the DEGs were enriched in the MAPK signaling pathway, PI3K - Akt signaling pathway. 7 genes (PPP2R1B, MYD88, CD86, CD80, MAP2K1, TRIB3 and ICOS) were screened by univariate and multivariate Cox regression, and they were used to construct a prognostic model. In the TCGA and GEO datasets, Kaplan-Meier analysis indicated that patients in the high-risk group have a poor prognosis. The sensitivity and specificity evaluation of prognostic model for 1-, 3-, 5-year OS in TCGA were 0.644, 0.661 and 0.625, respectively; and in GSE41613 were 0.748, 0.719, and 0.727, respectively. The calibration chart curve showed that the nomogram has strong clinical performance in the prognosis prediction of HNSCC patients. Conclusions A novel immune checkpoint-related gene signature can effectively predict and stratify OS in HNSCC patients.