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A novel immune-related gene signature predicts survival in esophageal squamous cell carcinoma

BACKGROUND: Immune-related genes (IRGs) are highly relevant to the progression and prognosis of esophageal squamous cell carcinoma (ESCC). A prognostic signature could be reliable in stratifying ESCC patients according to the risk score, which may help manage systematic treatments. In this study, a...

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Detalles Bibliográficos
Autores principales: Xu, Tao, Dai, Tianyang, Zeng, Peiyuan, Guo, Yunfen, He, Kaiming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8798451/
https://www.ncbi.nlm.nih.gov/pubmed/35116551
http://dx.doi.org/10.21037/tcr-20-2665
Descripción
Sumario:BACKGROUND: Immune-related genes (IRGs) are highly relevant to the progression and prognosis of esophageal squamous cell carcinoma (ESCC). A prognostic signature could be reliable in stratifying ESCC patients according to the risk score, which may help manage systematic treatments. In this study, a systematic and reliable immune signature was developed to estimate the prognostic stratification in ESCC. METHODS: Ribonucleic acid (RNA) expression data of 79 ESCC samples from the Cancer Genome Atlas (TCGA) database and 269 normal esophageal mucosal samples from the Genotype-Tissue Expression (GTEx) project database were downloaded from the University of California, Santa Cruz (UCSC) website to form a TCGA-GTEx dataset. First, we screened differentially expressed genes (DEGs) and then filtered IRGs based on the Immunology Database and Analysis Portal (ImmPort) database to obtain immune-related DEGs (IRDEGs). Next, a novel prognostic signature based on IRDEGs was developed using multivariable Cox analysis. Immune infiltration status was evaluated via single-sample gene set enrichment analysis (ssGSEA). ESCC tissues were grouped into three clusters in terms of immune infiltration (Immunity-L, Immunity-M, and Immunity-H) by applying an unsupervised hierarchical clustering algorithm. Finally, the samples were divided into high- and low-risk groups using the median of the risk score scores for GSEA pathway enrichment analysis in the three clusters. RESULTS: The prognostic signature based on IRDEGs (FCER1G, ISG20, and EGFR) performed moderately in prognostic predictions, with a concordance index (C-index) value of 0.73 [95% (confidence interval) CI: 0.63–0.84, P=2.02E-05] and an area under the curve (AUC) value of 0.817. The xenobiotic metabolism pathway was significantly enriched and up-regulated both in the high-risk group of the immunity-M and immunity-H clusters. CONCLUSIONS: The novel immune-related prognostic signature we constructed has a good prognostic, predictive ability and can be used as an independent prognostic indicator. Our study provides clinicians with a quantitative tool to predict the probability of individual survival time and helps clinicians select targets for immunotherapies and individualized treatment strategies for ESCC patients.