Dataset Information

Identification of a Costimulatory Molecule Gene Signature to Predict Survival and Immunotherapy Response in Head and Neck Squamous Cell Carcinoma.

ABSTRACT:

Background

Head and neck squamous cell carcinoma (HNSCC) is one of the most common malignancies worldwide. Checkpoint blockade immunotherapy has made tremendous progress in the treatment of a variety of cancers in recent years. Costimulatory molecules constitute the foundation of cancer immunotherapies and are deemed to be promising targets for cancer treatment. This study attempted to evaluate the potential value of costimulatory molecule genes (CMGs) in HNSCC.

Materials and methods

Based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) dataset, we identified the prognostic value of CMGs in HNSCC. Subsequently, CMGs-based signature (CMS) to predict overall survival of HNSCC patients was established and validated. The differences of downstream pathways, clinical outcomes, immune cell infiltration, and predictive immunotherapy responses between different CMS subgroups were investigated via bioinformatic algorithms. We also explored the biological functions of TNFRSF12A, one risk factor of CMS, by in vitro experiments.

Results

Among CMGs, 22 genes were related to prognosis based on clinical survival time in HNSCC. Nine prognosis-related CMGs were selected to establish CMS. CMS was an independent risk factor and could indicate the survival of HNSCC patients, the component of tumor-infiltrating lymphocytes, and the immunotherapy response rate. Functional enrichment analysis confirmed that CMS might involve immune-relevant processes. Additionally, TNFRSF12A was related to poor prognosis and enhanced malignant phenotype of HNSCC.

Conclusion

Collectively, CMS could accurately indicate prognosis, evaluate the tumor immune microenvironment, and predict possible immunotherapy outcomes for HNSCC patients.

SUBMITTER: Aye L

PROVIDER: S-EPMC8381651 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundHead and neck squamous cell carcinoma (HNSCC) is a common malignancy that emanates from the lips, mouth, paranasal sinuses, oropharynx, larynx, nasopharynx, and from other pharyngeal cancers. The availability of high-throughput expression data has made it possible to use global gene expression data to analyze the relationship between metabolic-related gene expression and clinical outcomes in HNSCC patients.MethodIn this study, we used RNA sequencing (RNA-seq) data from the cancer genome atlas (TCGA), with validation in the GEO dataset to profile the metabolic microenvironment and define potential biomarkers for metabolic therapy.ResultsWe extracted data for 529 patients and 327 metabolic genes (198 upregulated and 129 downregulated genes) in the TCGA database. Carbonic anhydrase 9 (CA9) and CA6 had the largest logFCs in the upregulated and downregulated genes, respectively. Our Cox regression model data showed 51 prognostic-related genes with lysocardiolipin acyltransferase 1 (LCLAT1) and choline dehydrogenase (CHDH) being associated with the highest risk (HR = 1.144, 95% CI = 1.044 ~ 1.251) and the lowest risk (HR = 0.580, 95% CI = 0.400 ~ 0.839) in HNSCC, respectively. We next used the ROC curve to evaluate whether the differentially expressed metabolic-related genes could serve as early predictors of HNSCC. The findings showed an AUC of 0.745 and 0.618 in the TCGA and GEO analysis, respectively. Besides, the ability for the genes to predict clinicopathological HNSCC status was analyzed and the data showed that the AUC for age, gender, grade, stage, T, M, and N was 0.520, 0.495, 0.568, 0.606, 0.577, 0.476, and 0.673, respectively, in the TCGA dataset. On the other hand, the AUC for age, gender, stage, T, M, N, smoking, and HPV16-pos was 0.599, 0.531, 0.622, 0.606, 0.616, 0.550, 0.614, 0.519, and 0.397, respectively, in the GEO dataset.ConclusionTaken together, our study unearths a novel metabolic gene signature for the prediction of HNSCC prognosis based on the TCGA dataset. Our signature might point out the metabolic microenvironment disorders and provides potential treatment targets and prognostic biomarkers.

Project description:Background and purposeHead and neck squamous carcinoma (HNSCC), characterized by immunosuppression, is a group of highly heterogeneous cancers. Although immunotherapy exerts a promising influence on HNSCC, the response rate remains low and varies in assorted primary sites. Immunological mechanisms underlying HNSCC pathogenesis and treatment response are not fully understood. This study aimed to develop a differentially expressed genes (DEGs)-based risk model to predict immunotherapy efficacy and stratify prognosis of HNSCC patients.Materials and methodsThe expression profiles of HNSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. The tumor microenvironment and immune response were estimated by cell type identification via estimating relative subset of known RNA transcripts (CIBERSORT) and immunophenoscore (IPS). The differential expression pattern based on human papillomavirus status was identified. A DEGs-based prognostic risk model was developed and validated. All statistical analyses were performed with R software (version 3.6.3).ResultsBy using the TCGA database, we identified DKK1, HBEGF, RNASE7, TNFRSF12A, INHBA, and IPIK3R3 as DEGs that were associated with patients' overall survival (OS). Patients were stratified into the high- and low-risk subgroups according to a DEGs-based prognostic risk model. Significant difference in OS was found between the high- and low-risk patients (1.64 vs. 2.18 years, P = 0.0017). In multivariate Cox analysis, the risk model was an independent prognostic factor for OS (hazard radio = 1.06, 95% confidence interval [1.02-1.10], P = 0.004). More CD8+ T cells and regulatory T cells were observed in the low-risk group and associated with a favorable prognosis. The IPS analysis suggested that the low-risk patients possessed a higher IPS score and a higher immunoreactivity phenotype, which were correlated with better immunotherapy response.ConclusionCollectively, we established a reliable DEGs-based risk model with potential prognostic value and capacity to predict the immunophenotype of HNSCC patients.