Seven Immune-Related Genes’ Prognostic Value and Correlation with Treatment Outcome in Head and Neck Squamous Cell Carcinoma

Abstract

Background. Head and neck squamous cell carcinoma (HNSCC) is a growing concern worldwide, due to its poor prognosis, low responsiveness to treatment, and drug resistance. Since immunotherapy effectively improves HNSCC patients’ survival status, it is important to continuously explore new immune-related predictive factors to accurately predict the immune landscape and clinical outcomes of individuals suffering from HNSCC. Methods. The HNSCC transcriptome profiling of RNA-sequencing data was retrieved from TCGA database, and the microarray of GSE27020 was obtained from the GEO database for validation. The differentially expressed genes (DEGs) between HNSCC and normal samples were identified by multiple test corrections in TCGA database. The univariate and multivariate Cox analyses were performed to identify proper immune-related genes (IRGs) to construct a risk model. The Cox regression coefficient was employed for calculation of the risk score (RS) of IRG signature. The median value of RS was utilized as a basis to classify individuals with HNSCC into high- and low-risk groups. The Kaplan-Meier (K-M) survival analysis and receiver operating characteristic (ROC) curves were employed for the identification of the prognostic significance and precision of the IRG signature. The signature was also evaluated based on clinical variables, predictive nomogram, mutation analysis, infiltrating immune cells, immune-related pathways, and chemotherapeutic efficacy. The protein-protein interaction (PPI) network and functional enrichment pathway investigations were utilized to explore possible potential molecular mechanisms. Finally, the hub gene’s differential mRNA expression levels were evaluated by means of the Gene Expression Profiling Interactive Analysis (GEPIA), and the Human Protein Atlas (HPA) was utilized for the validation of their translational levels. Results. Collectively, 1593 DEGs between HNSCC and normal samples were identified, of which 136 IRGs were differentially expressed. Then, the 136 immune-related DEGs were mostly enriched in the cytokine-related signaling pathways by GO and KEGG analyses. After that, a valuable signature based on seven genes (DKK1, GAST, IGHM, IL12RB2, SLURP1, STC2, and TNFRSF4) was designed. The HNSCC patients into the low-risk group and the high-risk group were divided by using the median RS; the HNSCC patients in the high-risk group had a worse survival than those in the low-risk group. The risk signature was verified to be an independent predictive marker for HNSCC patients. Meanwhile, the RS had the largest contribution to survival of these patients based on the predictive nomogram. In addition, the low-risk HNSCC patients exhibited significantly enriched immune cells, along with an association with high chemosensitivity. Conclusion. The constructed gene signature can independently function as a predictive indicator for the clinical features of HNSCC patients. The low-risk HNSCC subjects might benefit from immunotherapy and chemotherapy.