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In a cohort of 70 MDS patients and 10 matched controls, multicolor flow cytometry quantified monocytic (M-MDSC), early (e-MDSC), and granulocytic (G-MDSC) subsets alongside B, T, and NK cells, with WT1 transcripts assessed by RQ-PCR and burden stratified by CD34+ blasts and IPSS-R. Monocytic MDSC enriched with higher WT1 and CD34+ proportions, reduced baseline e-MDSC associated with longer survival, and distinct lymphoid interaction patterns supported actionable biomarkers for risk-adapted immunotherapy.",{"@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/mdsc-subpopulation-dynamics-predict-disease-evolution-a-multidimensional-biomarker-framework-for-risk-assessment-in-mds/440892/",{"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/mdsc-subpopulation-dynamics-predict-disease-evolution-a-multidimensional-biomarker-framework-for-risk-assessment-in-mds/440892.png","ImageObject",300,407,{"name":92,"@type":93},"Emma Wilson","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-01","2026-09-29",true,{"@type":102,"interactionType":103,"userInteractionCount":14},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What study question does the document address in MDSC research?","Question",{"text":112,"@type":113},"It investigates how MDSC subpopulations relate to tumor burden dynamics, leukemic progenitor characteristics, and immunophenotypic remodeling in myelodysplastic syndromes for prognostic biomarker discovery.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were MDSC subsets and biomarkers measured?",{"text":117,"@type":113},"Multicolor flow cytometry quantified M-MDSC, e-MDSC, and G-MDSC and lymphocyte subsets, while WT1 transcript levels were measured by RQ-PCR; tumor burden was stratified using CD34+ blast percentage and IPSS-R criteria.",{"name":119,"@type":110,"acceptedAnswer":120},"Which MDSC subset showed prognostic association with overall survival?",{"text":121,"@type":113},"Lower baseline e-MDSC levels (below 2.36%) correlated with prolonged median overall survival.","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},440892,1790893972,{"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":14,"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},3848291630094,"https://eur-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45","Clinical and Experimental Medicine (2026) 26:63  \n[https://doi.org/10.1007/s10238-025-01986-4](https://doi.org/10.1007/s10238-025-01986-4)  \nRESEARCH  \nMDSC subpopulation dynamics predict disease evolution: a  \nmultidimensional biomarker framework for risk assessment in MDS  \nZhongLi Hu1,3 · ZhongTing Hu2 · YongYu Zhang7 · MengQing Hua3 · ShaoJie Huang8 · YuXian Wang1 · YanLi Yang1 · Ping Zhao4 · Yu Zhou5,6  \nReceived: 2 April 2025 / Accepted: 24 November 2025 © The Author(s) 2025  \nAbstract  \nTo delineate subtype-specific associations of myeloid-derived suppressor cells (MDSCs) with tumor burden dynamics, leukemic progenitor features, and immunophenotypic remodeling in myelodysplastic syndromes (MDS), addressing unmet needs in prognostic biomarker discovery. In this cohort study of 70 MDS patients and 10 age-matched healthy controls, we performed multicolor flow cytometry to quantify monocytic (M-MDSC), early (e-MDSC), and granulocytic (G-MDSC) subsets alongside lymphocyte subpopulations (CD19 + B cells, CD3 + T cells, CD56 + NK cells) . WT1 transcript levels were assessed via RQ-PCR, with tumor burden stratified by CD34 + blast percentage and IPSS-R criteria. Subtype-specific analysis revealed selective enrichment of monocytic MDSC (M-MDSC) in patients including elevated WT1 transcript levels, and elevated CD34 + cell proportions. Reduced baseline e-MDSC levels (\u003C 2.36%) correlated with prolonged median overall survival (6.5vs4months; P = 0.0174) . Notably, granulocytic MDSC (G-MDSC) demonstrated modest diagnostic utility (AUC = 0.7350, P = 0.0167) but failed to stratify patients by IPSS-R risk categories. Mechanistically, MDSC subsets exhibited distinct lymphoid interaction patterns: M-MDSC expansion demonstrated a positive correlation with CD19 + B-cell frequencies (r =0.3051, P = 0.0102), while e-MDSC accumulation positively correlated with NK cells (r =0.37, P = 0.001) and inversely correlated with T cells (r=-0.2845, P = 0.0170) . M-MDSC and e-MDSC—but not G-MDSC—serve as clinically actionable biomarkers reflecting tumor burden and survival outcomes in MDS. Their distinct interactions with B, T, and NK lymphocytes implicate subset-specific immunosuppressive pathways, offering novel targets for risk-adapted immunotherapy.  \nKeywords Myeloid-derived suppressor cells · Myelodysplastic syndrome · T cells · NK cells · CD34 · LDH · WT1 · Immune phenotypes  \nZhongLi Hu, ZhongTing Hu, YongYu Zhang and MengQing Hua contributed equally to the study.  \n􀀍 YanLi Yang [Yangyanli0702@126.com](Yangyanli0702@126.com)  \n􀀍 Ping Zhao [414366791@qq.com](414366791@qq.com)  \n􀀍 Yu Zhou [364182189@qq.com](364182189@qq.com)  \n1 Department of Haematology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China  \n2 Office of Academic Research, Bengbu Medical University, Bengbu, Anhui, China  \n3 Key laboratory of chronic disease immunology basis and clinical, Bengbu Medical University, Bengbu, Anhui Province, China  \n4 Department of Rheumatology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China  \n5 Department of Obstetrics and Gynecology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China  \n6 Department of Obstetrics and Gynecology, The Second Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China  \n7 Departmeng of Oncology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, China  \n8 Department of Oral Medicine, Bengbu Medical University, Bengbu, Anhui, China  \n1 3  \nIntroduction  \nMyelodysplastic syndromes (MDS) represent a heterogeneous cluster of clonal hematopoietic stem cell neoplasms distinguished by dysplastic hematopoiesis, progressive cytopenias, and heightened propensity for leukemic evolution to acute myeloid leukemia (AML) [1] . The tumor immune microenvironment has been increasingly recognized as a central orchestrator of MDS pathogenesis, wherein chronic inflammatory states perpetuated by dysregulated immune re","cbCaicyvqgbBDytF","https://ap.wps.com/l/cbCaicyvqgbBDytF","pdf",2683229,15,"English","# Introduction\n## Clinical background of MDS and immune microenvironment\n## Role of MDSC subsets in immunosuppression","[{\"question\":\"What study question does the document address in MDSC research?\",\"answer\":\"It investigates how MDSC subpopulations relate to tumor burden dynamics, leukemic progenitor characteristics, and immunophenotypic remodeling in myelodysplastic syndromes for prognostic biomarker discovery.\"},{\"question\":\"How were MDSC subsets and biomarkers measured?\",\"answer\":\"Multicolor flow cytometry quantified M-MDSC, e-MDSC, and G-MDSC and lymphocyte subsets, while WT1 transcript levels were measured by RQ-PCR; tumor burden was stratified using CD34+ blast percentage and IPSS-R criteria.\"},{\"question\":\"Which MDSC subset showed prognostic association with overall survival?\",\"answer\":\"Lower baseline e-MDSC levels (below 2.36%) correlated with prolonged median overall survival.\"}]","MDSC subpopulation dynamics predict disease evolution - a multidimensional biomarker framework for risk assessment in MDS | PDF",1790693826,38]