ArticleImmunoTargets and therapy2025
Comprehensive Analyses of Single-Cell and Bulk RNA Sequencing Data From M2 Macrophages to Elucidate the Immune Prognostic Signature in Patients with Gastric Cancer Peritoneal Metastasis.
Article in ImmunoTargets and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Multimodal digital biopsy for preoperative prediction of occult peritoneal metastasis in gastric cancer.NPJ digital medicine · 2026Article
- Tumor-associated macrophages in gastric cancer peritoneal metastasis: orchestrating immune evasion, niche remodeling, and therapeutic resistance.Frontiers in immunology · 2026Review
- Machine Learning-Derived Neddylation Gene Signature for Predicting Prognosis and Immunotherapy Benefits in Colorectal Cancer.ImmunoTargets and therapy · 2025Article
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10 authors.
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Abstract
Purpose: The peritoneum is a common site of metastasis in gastric cancer (GC), associated with poor prognosis and significant morbidity. The proclivity of GCs to metastasize to the peritoneum has been hypothesized to occur due the latter's immunosuppressive microenvironment, such as stromal infiltration and M2 macrophage enrichment, which are associated with increased risk of PM. As far as we know, a model that can effectively predict the prognosis of patients with GCPM is still lacking. Consequently, we constructed a prognostic risk model based on M2 macrophages associated with gastric cancer peritoneal metastasis, aiming to enhance predictive precision and guide tailored therapeutic interventions. Methods: M2 macrophage-associated genes were identified in combination with marker genes from single-cell RNA sequencing (scRNA-seq) and modular genes from weighted gene coexpression network analysis (WGCNA). A prognostic model was constructed via LASSO analysis and validated in internal and external cohorts. We further compared the immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between patient groups stratified by risk to clarify the immune landscape in the GCPM. Results: Our study identified 38 M2 macrophage-related genes via single-cell and bulk RNA sequencing. We developed a prognostic model based on the expression levels of 4 signature genes: DAB2, SPARC, PLTP, and FOLR2. The feasibility of the model was validated with internal and external validation sets (TCGA, GSE62254 and IMvigor210). The model also supported the prediction results of prognosis on the basis of the immunohistochemical results. Notably, patients with higher risk scores had a lower proportion of MSI-H and TMB, a higher prevalence of stages III-IV, and a lower likelihood of responding favorably to immunotherapy. Conclusion: Our prognostic risk model could effectively predict the prognosis and response to chemo-immune therapy in patients with GCPM. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.
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