ArticleBiology direct2025
RGS16-driven cancer-associated fibroblasts promote esophageal squamous cell carcinoma progression via the MDK-SDC1 axis-mediated intercellular crosstalk.
Article in Biology direct, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Article
- Cell-free RNA Signatures Derived from the Tumor Microenvironment Predict Outcomes of CAR-T Therapy in Large B Cell Lymphoma.medRxiv : the preprint server for health sciences · 2026Article
- CTSB-positive tumor-associated macrophages shape prognosis and therapeutic response in lung adenocarcinoma.Translational oncology · 2026Article
- Immunosuppressive tumor microenvironment and immunotherapy resistance of esophageal carcinoma.Frontiers in immunology · 2026Review
- Integrated single-cell and spatial transcriptomics combined with whole-exome sequencing reveal key hub genes and epithelial heterogeneity in bladder cancer.Frontiers in oncology · 2025Article
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8 authors.
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Abstract
backgroundCancer-associated fibroblasts (CAFs) are critical components of the tumor microenvironment (TME) and have important roles in carcinogenesis and metastasis by facilitating cancer cell proliferation, angiogenesis, extracellular matrix (ECM) remodeling, and drug resistance. The purpose of this study is to extensively describe CAFs in esophageal cancer (ESCA) and develop a CAF-based prognostic risk score to predict clinical outcomes in patients with ESCA.
methodsData for single-cell RNA sequencing (scRNA-seq) were collected from the GEO database. The GEO and TCGA databases provided the bulk RNA-seq data and microarray data for esophageal squamous cell carcinoma (ESCC), respectively. The Seurat R program was used to identify CAF clusters from scRNA-seq data. Univariate Cox regression analysis was then used to identify prognosis-related genes linked to CAFs. Using 65 machine learning algorithms, a risk signature was created using CAF-related prognosis genes. We investigated the relationships between the CAF-related gene risk score and clinical features, mutation landscape, immunotherapy response, and medication sensitivity.
resultsUsing scRNA-seq analysis in ESCC, 4856 genes associated with CAF clusters were identified, 14 of which were selected to construct a prognostic risk signature. Functional validation revealed that overexpression of the regulator of G-protein signaling 16-nuclear factor (RGS16) in CAFs co-cultured with the ESCC cell line KYSE520 significantly increased cancer cell proliferation, invasion, and migration. The secreted midkine (MDK) coupled to its receptor syndecan1 (SDC1) on ESCC cells, further helping their malignant tendencies.
conclusionsComprehensive assessment of CAF heterogeneity in ESCC sheds light into the mechanisms of immunotherapy resistance and suggests prospective options for creating novel treatment therapies. Furthermore, we demonstrate that RGS16+ CAFs promote ESCC progression through the nuclear factor kappa B(NF-κB)-MDK-SDC1 axis, highlighting their crucial involvement in tumor-stromal interaction. These findings emphasize the therapeutic potential of targeting RGS16+ CAFs, which presents a promising method for disrupting the tumor-promoting microenvironment and improving clinical outcomes in ESCC patients. CLINICAL TRIAL NUMBER: Not applicable.
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