ArticleFrontiers in oncology2024
Immune cell related signature predicts prognosis in esophageal squamous cell carcinoma based on single-cell and bulk-RNA sequencing.
Article in Frontiers in oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A highly potent compound W-1-32 targeting XIAP protein inhibits proliferation and metastasis of esophageal squamous cell carcinoma.Frontiers in pharmacology · 2026Article
- Clonal evolution in gastrointestinal cancers: multi-omics insights into tumor heterogeneity, microenvironmental selection, and translational biomarkers.Frontiers in oncology · 2026Review
- Single-cell atlas of the esophageal squamous cell carcinoma immune ecosystem to predict immunotherapy response.Signal transduction and targeted therapy · 2025Article
- Alcohol consumption and esophageal cancer risk: unveiling DLEU2 as a key immune modulator through Mendelian randomization and transcriptomic analysis.Discover oncology · 2025Article
- Construction of a multigenic diagnostic, prognostic, and immune infiltration model with methylation-associated regulators in esophageal squamous cell carcinoma.Journal of thoracic disease · 2025Article
- Single-cell RNA sequencing reveals adrb1 as a sympathetic nerve-regulated immune checkpoint driving T cell exhaustion and impacting immunotherapy in esophageal squamous cell carcinoma.Frontiers in immunology · 2025Article
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7 authors.
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Abstract
Background: It has been reported that tumor immune microenvironment performs a vital role in tumor progress. However, acting mechanism of immune cell related genes (IRGs) in esophageal squamous cell carcinoma (ESCC) is uncertain. Methods: TCGA-ESCC, GSE23400, GSE26886, GSE75241, and GSE196756 datasets were gained via public databases. First, differentially expressed genes (DEGs) between ESCC and control samples from GSE23400, GSE26886, and GSE75241 were screened out by differential expression analysis, and overlapping DEGs were identified. Single-cell transcriptome data of GSE196756 were applied to explore immune cells that might be involved in regulation of ESCC. Then, weighted gene co-expression network analysis was applied to screen IRGs. Next, differentially expressed IRGs (DE-IRGs) were identified by overlapping IRGs and DEGs, and were incorporated into univariate Cox, least absolute shrinkage and selection operator, and multivariate Cox to acquire prognosis-related genes, and ESCC samples were grouped into high-/low-risk groups on the basis of median risk score. Finally, the role of prognosis model in immunotherapy was analyzed. Results: Totally 248 DEGs were yielded by overlapping 3,915 DEGs in GSE26886, 459 DEGs in GSE23400, and 1,641 DEGs in GSE75241. Single-cell analysis found that B cells, dendritic cells, monocytes, neutrophils, natural killer cells, and T cells were involved in ESCC development. Besides, MEred, MEblack, MEpink, MEblue and MEbrown modules were considered as key modules because of their highest correlations with immune cell subtypes. A total of 154 DE-IRGs were yielded by taking intersection of DEGs and genes in key modules. Moreover, CTSC, ALOX12, and RMND5B were identified as prognosis-related genes in ESCC. Obviously, Exclusion and TIDE scores were notably lower in high-risk group than in the other one, indicating that high-risk group was more responsive to immunotherapy. Conclusions: Through bioinformatic analysis, we identified a prognosis model consisting of IRGs (CTSC, ALOX12, and RMND5B) in ESCC, providing new ideas for studies related to treatment and prognosis of ESCC.
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