ArticleFrontiers in immunology2022
Tumor Microenvironment Characterization for Assessment of Recurrence and Survival Outcome in Gastric Cancer to Predict Chemotherapy and Immunotherapy Response.
Article in Frontiers in immunology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- The application of random forest-based models in prognostication of gastrointestinal tract malignancies: a systematic review.Frontiers in artificial intelligence · 2025Pooled it
- The prognostic value of the tertiary lymphoid structure in gastrointestinal cancers.Frontiers in immunology · 2023Pooled it
- Predictors of immunotherapy response in gastric cancer: the role of the tumor microenvironment and integrative predictive models.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026Review
- Translational deep learning models for risk stratification to predict prognosis and immunotherapy response in gastric cancer using digital pathology.Journal of translational medicine · 2025Article
- Multiplex gene-editing strategy to engineer allogeneic EGFR-targeting CAR T-cells with improved efficacy against solid tumors.Nature communications · 2025Article
- Interpretable Multimodal Fusion Model Enhances Postoperative Recurrence Prediction in Gastric Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Molecular subtyping combined with multiomics analysis to study correlation between TACE refractoriness and tumor stemness in hepatocellular carcinoma.Discover oncology · 2025Article
- Cell-in-cell associated lncRNA signature predicts prognosis and immunotherapy response in gastric cancer.Frontiers in oncology · 2025Article
- Research advances in the molecular classification of gastric cancer.Cellular oncology (Dordrecht, Netherlands) · 2024Review
- Article
- Development and interpretation of a pathomics-driven ensemble model for predicting the response to immunotherapy in gastric cancer.Journal for immunotherapy of cancer · 2024Article
- Dissecting gastric cancer heterogeneity and exploring therapeutic strategies using bulk and single-cell transcriptomic analysis and experimental validation of tumor microenvironment and metabolic interplay.Frontiers in pharmacology · 2024Article
- Artificial intelligence annotated clinical-pathologic risk model to predict outcomes of advanced gastric cancer.Frontiers in oncology · 2023Article
- Identification of cuproptosis-related subtypes, construction of a prognosis model, and tumor microenvironment landscape in gastric cancer.Frontiers in immunology · 2022Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The tumor microenvironment (TME) is crucial for tumor recurrence, prognosis, and therapeutic responses. We comprehensively investigated the TME characterization associated with relapse and survival outcomes of gastric cancer (GC) to predict chemotherapy and immunotherapy response. Methods: A total of 2,456 GC patients with complete gene-expression data and clinical annotations from twelve cohorts were included. The TME characteristics were evaluated using three proposed computational algorithms. We then developed a TME-classifier, a TME-cluster, and a TME-based risk score for the assessment of tumor recurrence and prognosis in patients with GC to predict chemotherapy and immunotherapy response. Results: Patients with tumor recurrence presented with inactive immunogenicity, namely, high infiltration of tumor-associated stromal cells, low infiltration of tumor-associated immunoactivated lymphocytes, high stromal score, and low immune score. The TME-classifier of 4 subtypes with distinct clinicopathology, genomic, and molecular characteristics was significantly associated with tumor recurrence (P = 0.002), disease-free survival (DFS, P <0.001), and overall survival (OS, P <0.001) adjusted by confounding variables in 1,193 stage I-III GC patients who underwent potential radical surgery. The TME cluster and TME-based risk score can also predict DFS (P <0.001) and OS (P <0.001). More importantly, we found that patients in the TMEclassifier-A, TMEclassifier-C, and TMEclassifier-D groups benefited from adjuvant chemotherapy, and patients in the TMEclassifier-B group without chemotherapy benefit responded best to pembrolizumab treatment (PD-1 inhibitor), followed by patients in the TMEclassifier-A, while patients in the C and D groups of the TMEclassifier responded poorly to immunotherapy. Conclusion: We determined that TME characterization is significantly associated with tumor recurrence and prognosis. The TME-classifier we proposed can guide individualized chemotherapy and immunotherapy decision-making.
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