[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125178-en":3,"doc-seo-125178-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125178,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",7,"Healthcare","Development of a Diagnostic Model for Focal Segmental Glomerulosclerosis - Integrating Machine Learning on Activated Pathways and Clinical Validation","Focal segmental glomerulosclerosis (FSGS) remains a major global health challenge, with rising incidence alongside evolving diagnostic methods and increasing chronic disease burden. This study improves FSGS diagnostic accuracy by integrating machine learning to identify activated pathways, followed by clinical validation. Data from 163 FSGS patients and 42 living donors across multiple GEO cohorts were batch-corrected using ComBat, pathways refined via GSEA, and a diagnostic model built using nine algorithms with external verification. Six genes were highlighted as biomarkers.","International Journal of General Medicine downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nInternational Journal of General Medicine  \n Open Access Full Text Article  \nORIGINAL RESEARCH  \nDevelopment of a Diagnostic Model for Focal Segmental Glomerulosclerosis: Integrating Machine Learning on Activated Pathways and Clinical Validation  \nYating Ge 1 , 2 , *, Xueqi Liu 1 , 3 , *, Jinlian Shu 1 , 2 , Xiao Jiang 1 , 3 , Yonggui Wu 1 , 3  \n1The Department of Nephrology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, People’s Republic of China; 2Department of Nephrology, The Second People’s Hospital of Hefei, Hefei Hospital Affiliated to Anhui Medical University, Hefei, Anhui, People’s Republic of China; 3Center for Scientific Research of Anhui Medical University, Hefei, Anhui, People’s Republic of China  \n*These authors contributed equally to this work  \nCorrespondence: Yonggui Wu, The Department of Nephrology, The First Affiliated Hospital of Anhui Medical University, Hefei, People’s Republic of China, [Email wuyonggui@medmail.com.cn](Email wuyonggui@medmail.com.cn)  \n\n| Background: Focal segmental glomerulosclerosis (FSGS) represents a major global health challenge, with its incidence rising in parallel with advances in diagnostic techniques and the growing prevalence of chronic diseases. This study seeks to enhance the diagnostic accuracy of FSGS by integrating machine learning approaches to identify activated pathways, complemented by robust clinical validation.\u003Cbr>Methods: We analyzed data from 163 FSGS patients and 42 living donors across multiple GEO cohorts via the ComBat algorithm to address batch effects and ensure the comparability of gene expression profiles. Gene set enrichment analysis (GSEA) identified key signaling pathways involved in FSGS pathogenesis. We then developed a highly accurate diagnostic model by integrating nine machine learning algorithms into 101 combinations, achieving near-perfect AUC values across training, validation, and external cohorts. The model identified six genes as potential biomarkers for FSGS. Additionally, immune cell infiltration patterns, particularly those involving natural killer (NK) cells, were explored, revealing the complex interplay between genetics and the immune response in FSGS patients. Immunohistochemical analysis validated the expression of the key markers CD99 and OAZ2 and confirmed the association between NK cells and FSGS.\u003Cbr>Results: The glmBoost+Ridge model exhibited exceptional diagnostic accuracy, achieving an AUC of 0.998 using just six genes: BANF1, TUSC2, SMAD3, TGFB1, CD99, and OAZ2 . The prediction score was calculated as follows: score = (0.3997×BANF1) +(0.5543×TUSC2) + (0.5279×SMAD3) + (0.4118×TGFB1) + (0.8665×CD99) + (0.5996×OAZ2) . Immunohistochemical analysis confirmed significantly elevated expression levels of CD99 and OAZ2 in the glomeruli and tubulointerstitial tissues of FSGS patients compared with those of controls.\u003Cbr>Conclusion: This study demonstrates a highly accurate machine learning model for FSGS diagnosis. Immunohistochemical validation confirmed elevated expression of CD99 and OAZ2, offering valuable insights into FSGS pathogenesis and potential biomarkers for clinical application.\u003Cbr>Keywords: focal segmental glomerulosclerosis (FSGS), machine learning diagnostic model, gene set enrichment analysis (GSEA), immune cell infiltration |\n| --- |\n| Introduction\u003Cbr>Focal segmental glomerulosclerosis (FSGS) represents a significant global health concern characterized by varying incidence rates and prevalence rates across different regions and populations.1–3 Recently, an increasing trend in the incidence ofFSGS has been reported,4,5 which is likely linked to advancements in diagnostic techniques, an aging population, and an increasing |\n\nReceived: 28 September 2024  \nAccepted: 18 February 2025  \nPublished: 26 February 2025  \nInternational Journal of General Medicine 2025","cbCait1c8PC8IRze","https://ap.wps.com/l/cbCait1c8PC8IRze","pdf",8766410,1,16,"English","en",105,"# Background\n# Methods\n## Data integration and batch effect correction\n## Pathway identification\n## Diagnostic model construction and validation\n## Biomarker and immune infiltration analysis\n# Results\n## Model performance and key genes\n## Immunohistochemical validation\n# Conclusion","[{\"question\":\"How was batch effect handled when integrating GEO cohorts?\",\"answer\":\"The study used the ComBat algorithm to address batch effects and ensure comparability of gene expression profiles across cohorts.\"},{\"question\":\"Which diagnostic model achieved the highest accuracy and what were the six genes?\",\"answer\":\"The glmBoost+Ridge model showed near-perfect performance with an AUC of 0.998 using six genes: BANF1, TUSC2, SMAD3, TGFB1, CD99, and OAZ2.\"},{\"question\":\"How were the candidate biomarkers validated clinically?\",\"answer\":\"Immunohistochemical analysis confirmed significantly elevated expression of CD99 and OAZ2 in glomeruli and tubulointerstitial tissues of FSGS patients versus controls, supporting their diagnostic relevance.\"}]","Development of a Diagnostic Model for Focal Segmental Glomerulosclerosis - Integrating Machine Learning on Activated Pathways and Clinical Validation | PDF",1785897229,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"development-of-a-diagnostic-model-for-focal-segmental-glomerulosclerosis-integrating-machine-learning-on-activated-pathways-and-clinical-validation","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/development-of-a-diagnostic-model-for-focal-segmental-glomerulosclerosis-integrating-machine-learning-on-activated-pathways-and-clinical-validation/125178/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How was batch effect handled when integrating GEO cohorts?","Question",{"text":75,"@type":76},"The study used the ComBat algorithm to address batch effects and ensure comparability of gene expression profiles across cohorts.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which diagnostic model achieved the highest accuracy and what were the six genes?",{"text":80,"@type":76},"The glmBoost+Ridge model showed near-perfect performance with an AUC of 0.998 using six genes: BANF1, TUSC2, SMAD3, TGFB1, CD99, and OAZ2.",{"name":82,"@type":73,"acceptedAnswer":83},"How were the candidate biomarkers validated clinically?",{"text":84,"@type":76},"Immunohistochemical analysis confirmed significantly elevated expression of CD99 and OAZ2 in glomeruli and tubulointerstitial tissues of FSGS patients versus controls, supporting their diagnostic relevance.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,117,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":116},"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]