ArticleFrontiers in bioengineering and biotechnology2022
Identification and Validation of a Prognostic Immune-Related Gene Signature in Esophageal Squamous Cell Carcinoma.
Article in Frontiers in bioengineering and biotechnology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Advances in multi-omics for esophageal squamous cell carcinoma: diagnostic, prognostic, and therapeutic perspectives.Protein & cell · 2026Review
- Multi-cohort validation based on a novel prognostic signature of anoikis for predicting prognosis and immunotherapy response of esophageal squamous cell carcinoma.Frontiers in oncology · 2025Article
- A novel computational model ITHCS for enhanced prognostic risk stratification in ESCC by correcting for intratumor heterogeneity.Briefings in bioinformatics · 2024Article
- LINC01305 recruits basonuclin 1 to act on G-protein pathway suppressor 1 to promote esophageal squamous cell carcinoma.Cancer science · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
Esophageal carcinoma (EC) is a common malignant cancer worldwide. Esophageal squamous cell carcinoma (ESCC), the main type of EC, is difficult to treat because of the widespread morbidity, high fatality rates, and low quality of life caused by postoperative complications and no specific molecular target. In this study, we screened genes to establish a prognostic model for ESCC. The transcriptome expression profiles of 81 ESCC tissues and 340 normal esophageal mucosal epithelium tissues were obtained from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) cohorts. The transcriptome expression datasets of 19 esophageal squamous carcinoma cell lines were downloaded from Cancer Cell Line Encyclopedia (CCLE). The R software Limma package was used to identify 6,231 differentially expressed genes and 647 differentially expressed immune-related genes between normal and ESCC tissues. Gene functional analysis was performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Weighted gene co-expression network analysis (WGCNA) was used to screen out 18 immune-related prognostic genes. We then established the prognostic and risk signature using these genes, and the patients were divided into low-risk and high-risk groups. Compared with high-risk group patients, the low-risk group patients had longer overall survival. M1 macrophages and resting dendritic cells were differentially distributed between the low-risk and high-risk groups and were related to patient survival. We also examined the functional immune cell and immune molecule levels in low-risk and high-risk group patients, with significant differences in the tumor microenvironment between the two groups. To further verify the accuracy of the prognostic risk model, we performed area under the ROC curve (AUC) analysis. The AUC value was 0.931 for the prognostic risk, which was better than the microsatellite instability (MSI) and Tumor Immune Dysfunction and Exclusion (TIDE) scores. In conclusion, we found 18 immune-related prognostic genes related to the occurrence of ESCC and established a prognostic model for predicting disease severity.
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