ArticleAging2021
Tumor microenvironment characterization in esophageal cancer identifies prognostic relevant immune cell subtypes and gene signatures.
Article in Aging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Immune exhaustion in esophageal cancer: interferon pathway dysregulation and neoadjuvant therapy response.Frontiers in cell and developmental biology · 2026Review
- Review
- Biomarker identification associated with M2 tumor-associated macrophage infiltration in glioblastoma.Frontiers in neurology · 2025Article
- Prognostic model construction and immune microenvironment analysis of esophageal cancer based on gene expression data and microRNA target genes.Translational cancer research · 2023Article
- Systematic review and meta-analysis of endoscopic ultrasonography in staging diagnosis of esophageal cancer after neoadjuvant radiotherapy and chemotherapy.Journal of gastrointestinal oncology · 2022Article
- Identification of Downregulated Exosome-Associated Gene ENPP1 as a Novel Lipid Metabolism and Immune-Associated Biomarker for Hepatocellular Carcinoma.Journal of oncology · 2022Article
- A novel immune-related radioresistant lncRNAs signature based model for risk stratification and prognosis prediction in esophageal squamous cell carcinoma.Frontiers in genetics · 2022Article
Corrections and comments
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4 authors.
Funding
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Abstract
Esophageal cancer (ESCA) is a common malignancy in the digestive system with a high mortality rate and poor prognosis. Tumor microenvironment (TME) plays an important role in the tumorigenesis, progression and therapy resistance of ESCA, whereas its role in predicting clinical outcomes has not been fully elucidated. In this study, we comprehensively estimated the TME infiltration patterns of 164 ESCA patients using Gene Set Variation Analysis (GSVA) and identified 4 key immune cells (natural killer T cell, immature B cell, natural killer cell, and type 1 T helper cell) associated with the prognosis of ESCA patients. Besides, two TME groups were defined based on the TME patterns with different clinical outcomes. According to the expression gene set between two TME groups, we built a model to calculate TMEscore based on the single-sample gene-set enrichment analysis (ssGSEA) algorithm. TMEscore systematically correlated the TME groups with genomic characteristics and clinicopathologic features. In conclusion, our data provide a novel TMEscore which can be regarded as a reliable index for predicting the clinical outcomes of ESCA.
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