Meta_B cells: a computationally identified candidate immunosuppressive driver of gastric cancer metastasis revealed by single-cell analysis and machine learning
Abstract Background Gastric cancer (GC) metastasis remains a major clinical challenge due to insufficient understanding of tumor microenvironment (TME) dynamics. While B cells are implicated in GC progression, their subset-specific roles in metastatic niches are poorly defined. Methods We analyzed g...
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| Format: | Article |
| Language: | English |
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Springer
2025-08-01
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| Series: | Discover Oncology |
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| Online Access: | https://doi.org/10.1007/s12672-025-03356-8 |
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| author | Tianchi Lei Yiwen Jiang Kexin Yang Chuqi Meng Yue An |
| author_facet | Tianchi Lei Yiwen Jiang Kexin Yang Chuqi Meng Yue An |
| author_sort | Tianchi Lei |
| collection | DOAJ |
| description | Abstract Background Gastric cancer (GC) metastasis remains a major clinical challenge due to insufficient understanding of tumor microenvironment (TME) dynamics. While B cells are implicated in GC progression, their subset-specific roles in metastatic niches are poorly defined. Methods We analyzed gastric cancer (GC) single-cell RNA-seq data from the GEO database (GSE163558), complemented by bulk RNA-seq analysis of TCGA-STAD cohorts. Meta_B cells were identified through Seurat clustering and validated in colorectal cancer metastases (GSE166555). And we constructed a prognostic model via hdWGCNA and LASSO-Cox regression. Functional analyses included GSEA, pseudotime trajectory (Monocle2) and cell-cell communication (CellChat). Results We identified meta_B cells, a metastasis-enriched B cell subset, characterized by CLEC2B/YBX3 overexpression. Functional analyses suggested a potential immunosuppressive role associated with computational inference of BTLA-TNFRSF14 pathway activation, correlating with interactions with macrophages and other immune cells. A machine learning-derived 10-gene prognostic model effectively stratified high-risk patients with stromal-rich tumor microenvironments and predicted potential enhanced chemosensitivity to axitinib, dasatinib, olaparib, rapamycin, and ribociclib. Conclusions Meta_B cells may represent a novel B cell subset computationally associated with immunosuppression and GC metastasis potentially mediated by the BTLA axis. Our integrative transcriptomic framework provides hypothesis-generating insights into metastatic TME remodeling and a clinically actionable tool for prognostic prediction. Targeting meta_B cells can be explored as a strategy to potentially overcome immunotherapy resistance. |
| format | Article |
| id | doaj-art-daeac5ee0b8b4826bba19640d087a89b |
| institution | Kabale University |
| issn | 2730-6011 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Oncology |
| spelling | doaj-art-daeac5ee0b8b4826bba19640d087a89b2025-08-20T04:02:56ZengSpringerDiscover Oncology2730-60112025-08-0116112010.1007/s12672-025-03356-8Meta_B cells: a computationally identified candidate immunosuppressive driver of gastric cancer metastasis revealed by single-cell analysis and machine learningTianchi Lei0Yiwen Jiang1Kexin Yang2Chuqi Meng3Yue An4First Clinical College, China Medical UniversityFirst Clinical College, China Medical UniversityDepartment of Surgical oncology, The First Hospital of China Medical UniversityDepartment of Gastroenterology, The First Hospital of China Medical UniversityDepartment of Gastroenterology, The First Hospital of China Medical UniversityAbstract Background Gastric cancer (GC) metastasis remains a major clinical challenge due to insufficient understanding of tumor microenvironment (TME) dynamics. While B cells are implicated in GC progression, their subset-specific roles in metastatic niches are poorly defined. Methods We analyzed gastric cancer (GC) single-cell RNA-seq data from the GEO database (GSE163558), complemented by bulk RNA-seq analysis of TCGA-STAD cohorts. Meta_B cells were identified through Seurat clustering and validated in colorectal cancer metastases (GSE166555). And we constructed a prognostic model via hdWGCNA and LASSO-Cox regression. Functional analyses included GSEA, pseudotime trajectory (Monocle2) and cell-cell communication (CellChat). Results We identified meta_B cells, a metastasis-enriched B cell subset, characterized by CLEC2B/YBX3 overexpression. Functional analyses suggested a potential immunosuppressive role associated with computational inference of BTLA-TNFRSF14 pathway activation, correlating with interactions with macrophages and other immune cells. A machine learning-derived 10-gene prognostic model effectively stratified high-risk patients with stromal-rich tumor microenvironments and predicted potential enhanced chemosensitivity to axitinib, dasatinib, olaparib, rapamycin, and ribociclib. Conclusions Meta_B cells may represent a novel B cell subset computationally associated with immunosuppression and GC metastasis potentially mediated by the BTLA axis. Our integrative transcriptomic framework provides hypothesis-generating insights into metastatic TME remodeling and a clinically actionable tool for prognostic prediction. Targeting meta_B cells can be explored as a strategy to potentially overcome immunotherapy resistance.https://doi.org/10.1007/s12672-025-03356-8Gastric cancer metastasisScRNA-seqMachine learning prognostic modelTumor microenvironmentImmune escapeChemotherapy sensitivity |
| spellingShingle | Tianchi Lei Yiwen Jiang Kexin Yang Chuqi Meng Yue An Meta_B cells: a computationally identified candidate immunosuppressive driver of gastric cancer metastasis revealed by single-cell analysis and machine learning Discover Oncology Gastric cancer metastasis ScRNA-seq Machine learning prognostic model Tumor microenvironment Immune escape Chemotherapy sensitivity |
| title | Meta_B cells: a computationally identified candidate immunosuppressive driver of gastric cancer metastasis revealed by single-cell analysis and machine learning |
| title_full | Meta_B cells: a computationally identified candidate immunosuppressive driver of gastric cancer metastasis revealed by single-cell analysis and machine learning |
| title_fullStr | Meta_B cells: a computationally identified candidate immunosuppressive driver of gastric cancer metastasis revealed by single-cell analysis and machine learning |
| title_full_unstemmed | Meta_B cells: a computationally identified candidate immunosuppressive driver of gastric cancer metastasis revealed by single-cell analysis and machine learning |
| title_short | Meta_B cells: a computationally identified candidate immunosuppressive driver of gastric cancer metastasis revealed by single-cell analysis and machine learning |
| title_sort | meta b cells a computationally identified candidate immunosuppressive driver of gastric cancer metastasis revealed by single cell analysis and machine learning |
| topic | Gastric cancer metastasis ScRNA-seq Machine learning prognostic model Tumor microenvironment Immune escape Chemotherapy sensitivity |
| url | https://doi.org/10.1007/s12672-025-03356-8 |
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