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Multiplex immunofluorescence and integration of single-cell RNA with bulk RNA sequencing pinpoint DAB2+ tumor-associated macrophages and ACTA2+ myCAFs as key contributors. NicheNet analysis implicates the PLAU-PLAUR signaling axis in macrophage–fibroblast–tumor communication, and the resulting spatial pattern is used for prognosis prediction and patient stratification.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/multi-dimensional-omics-integrated-machine-learning-framework-identifies-macrophage-fibroblast-tumor-coinltration-patterns-to-predict-prognosis-in-gastric-cancer/450289/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/multi-dimensional-omics-integrated-machine-learning-framework-identifies-macrophage-fibroblast-tumor-coinltration-patterns-to-predict-prognosis-in-gastric-cancer/450289.png","ImageObject",300,407,{"name":92,"@type":93},"Riley","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-03","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":81},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What spatial niche is associated with macrophage-fibroblast-tumor co-infiltration in gastric cancer?","Question",{"text":112,"@type":113},"A niche enriched with fibroblasts and macrophages shows a striking spatial co-infiltration pattern with tumor cells dominant in other niches.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which molecular/signaling axis is identified as central for macrophage–fibroblast–tumor communication?",{"text":117,"@type":113},"NicheNet analysis highlights the PLAU-PLAUR signaling axis as a central regulatory pathway linking macrophages, fibroblasts, and tumor cells.",{"name":119,"@type":110,"acceptedAnswer":120},"How is the Gastric-Discovery machine learning framework constructed and what is its purpose?",{"text":121,"@type":113},"The framework uses transfer learning based on an ImageNet pre-trained ResNet-50 model to accurately recognize the macrophage–fibroblast co-infiltration pattern, supporting prognosis prediction and precise patient stratification.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},450289,1791007414,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":81,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1374391975076,"https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051","npj | digital medicine Article  \nPublished in partnership with Seoul National University Bundang Hospital  \n[https://doi.org/10.1038/s41746-025-02179-9](https://doi.org/10.1038/s41746-025-02179-9)  \nMulti-dimensional omics integrated machine learning framework identifies macrophage-fibroblast-tumor coinfi ltration patterns to predict prognosis in gastric cancer  \n Check for updates  \n\n| Qi Wang1,7, Yuan Ni2,7, Sheng Lu3,7, Benyan Zhang4, Jun Ji5, Qu Cai1, Chao Yan3, Feng Qi1, Min Shi1,6  & Jun Zhang1,6  |  |\n| --- | --- |\n| Gastric cancer (GC), of which cases with peritoneal metastasis are particularly challenging, retains its position of being highly complex and remarkably resistant to therapy. Understanding the spatial heterogeneity and leveraging recent technologies such as machine learning to uncover explanatory patterns remains critical to truly understanding this disease. Here, we conducted spatial transcriptomics analysis to identify distinct niches within GC tissues. Among these, a niche enriched with fibroblasts and macrophages exhibited a striking spatial co-infiltration pattern with tumor cells dominant niches. Further validation by multiplex immunofluorescence highlighted the coordinated cellular interactions that characterize the TME. Through integration of sc-RNA with bulk RNA sequencing, we identified DAB2⁺ TAMs and ACTA2⁺ myCAFs as the main contributors to this coinfiltration pattern. NicheNet analysis further revealed that the PLAU-PLAUR signaling axis holds a central regulatory role in the communication between macrophages, fibroblasts and tumor cells. Given the prognostic value of this spatial pattern, we additionally applied transfer learning based on an ImageNet pre-trained ResNet-50 model to develop a machine learning framework that can accurately recognize the macrophage-fibroblast-malignant cell co-infiltration pattern, called Gastric-Discovery. Potentially, Gastric-Discovery could be a tool for precise patient stratification and provides novel insights into the dynamic architecture of the TME. |  |\n| Gastric cancer (GC) ranks among the main cancer-related causes of mortality globally and demonstrates a marked elevated incidence in China and other East Asian countries, posing a considerable burden on regional healthcare systems. A range of underlying factors have been identified as contributing to this complicated situation, among them the striking tendency ofmetastatic potential, with peritoneal metastasis beingboth the most prevalent and the most challenging form of the disease to manage1. Peritoneal metastasis uniquely resists systemic chemotherapy due to the drug- | blocking peritoneal barrier1,2. Commonly, GCPM exhibit extensive proliferation of fibrous connective tissue and an unusually rich matrix composition. Not only does this increase the aggressiveness of the lesions, but it also promotesa highly immunosuppressiveTME. Consequently, traditional treatment approaches that focus on the direct destruction of tumor cells have emerged as inadequate in this context3,4. Faced with this therapeutic impasse, clinical researchers persistently sought out novel approaches. Several treatment strategies have been proposed and refined through |\n\n1Department of Oncology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 2College of Life Sciences, Anhui University of Chinese Medicine, Hefei, China. 3Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 4Department of Pathology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 5Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. 6Shanghai Key Laboratory of Gastric Neoplasms, Shanghai, China. 7These authors contributed equally: Qi Wang, Yuan Ni, Sheng Lu. e-mail: sm1[1998@rjh.com.cn](1998@rjh.com.cn); [junzhang10977@sjtu.edu.cn](junzhang10977@sjtu.edu.cn)  \ncontinuous practice. ","cbCaihXOoBRl5QsA","https://ap.wps.com/l/cbCaihXOoBRl5QsA","pdf",24094822,18,"English","# Background\n## Peritoneal metastasis challenges\n## Need for spatial and computational approaches\n# Methods\n## Spatial transcriptomics for niche identification\n## Multiplex immunofluorescence validation\n## Integration of sc-RNA with bulk RNA sequencing\n## NicheNet signaling analysis\n## Transfer learning framework development (Gastric-Discovery)\n# Results\n## Fibroblast–macrophage enriched co-infiltration niche\n## Key cell contributors and signaling axis\n## Prognostic and predictive value\n# Potential impact\n## Precise patient stratification and TME architecture insights","[{\"question\":\"What spatial niche is associated with macrophage-fibroblast-tumor co-infiltration in gastric cancer?\",\"answer\":\"A niche enriched with fibroblasts and macrophages shows a striking spatial co-infiltration pattern with tumor cells dominant in other niches.\"},{\"question\":\"Which molecular/signaling axis is identified as central for macrophage–fibroblast–tumor communication?\",\"answer\":\"NicheNet analysis highlights the PLAU-PLAUR signaling axis as a central regulatory pathway linking macrophages, fibroblasts, and tumor cells.\"},{\"question\":\"How is the Gastric-Discovery machine learning framework constructed and what is its purpose?\",\"answer\":\"The framework uses transfer learning based on an ImageNet pre-trained ResNet-50 model to accurately recognize the macrophage–fibroblast co-infiltration pattern, supporting prognosis prediction and precise patient stratification.\"}]","Multi-dimensional omics integrated machine learning framework identifies macrophage-fibroblast-tumor coinfiltration patterns to predict prognosis in gastric cancer | PDF",1790732767,45]