[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-332119-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-332119-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","identification-and-validation-of-biomarkers-in-membranous-nephropathy-and-pan-cancer-analysis","Identification and validation of biomarkers in membranous nephropathy and pan-cancer analysis","","Membranous nephropathy (MN) is an autoimmune renal disorder and a leading cause of nephrotic syndrome in adults. Although links between MN and cancer have been reported, the mechanisms and reliable biomarkers remain unclear. The study integrates single-cell and bulk RNA-seq MN datasets for differential expression, hub gene discovery, ROC-based diagnostics, and ssGSEA immune infiltration profiling. Pan-cancer validation uses TCGA and GTEx to assess expression, mutation, drug sensitivity, and prognosis, then confirms six hub genes via immunohistochemistry on human renal biopsy tissues.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/identification-and-validation-of-biomarkers-in-membranous-nephropathy-and-pan-cancer-analysis/332119/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/identification-and-validation-of-biomarkers-in-membranous-nephropathy-and-pan-cancer-analysis/332119.png","ImageObject",300,407,{"name":42,"@type":43},"Ivy","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-06","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",6,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What datasets were used to identify biomarkers for membranous nephropathy (MN)?","Question",{"text":63,"@type":64},"Two MN datasets were acquired and integrated: a single-cell dataset and a bulk RNA-seq dataset from the Gene Expression Omnibus database for differential expression and downstream analysis.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were hub genes for MN selected and evaluated diagnostically?",{"text":68,"@type":64},"Hub genes were obtained using LASSO and random forest algorithms. Their diagnostic ability was assessed with ROC curves.",{"name":70,"@type":61,"acceptedAnswer":71},"How were the identified hub genes validated in this study?",{"text":72,"@type":64},"The six hub genes were validated through immunohistochemical analysis using human renal biopsy tissues, after computational pan-cancer and immune infiltration analyses.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},332119,1790219171,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":144,"read_time":145,"preview_page_count":4},549758252649,"https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819","TYPE Original Research PUBLISHED 23 May 2024  \nDOI 10.3389/fimmu.2024.1302909  \nOPEN ACCESS  \nEDITED BY  \nMario Ollero,  \nINSERM U955 Institut Mondor de Recherche Biome´ dicale (IMRB), France  \nREVIEWED BY Shuwang Ge,  \nHuazhong University of Science and Technology, China  \nUdhaya Kumar,  \nBaylor College of Medicine, United States  \n*CORRESPONDENCE Qian-qian Xu  \n[xqq816926@163.com](xqq816926@163.com)[ ](xqq816926@163.com)Wen-ge Li  \n[wenge_lee2002@126.com](wenge_lee2002@126.com)  \n†These authors have contributed equally to this work and share last authorship  \nRECEIVED 27 September 2023  \nACCEPTED 10 May 2024  \nPUBLISHED 23 May 2024  \nCITATION  \nYang Y, Zou G-m, Wei X-s, Zhang Z, Zhuo L, Xu Q-q and Li W-g (2024) Identification and validation of biomarkers in membranous nephropathy and pan-cancer analysis.  \nFront. Immunol. 15:1302909 .  \ndoi: 10.3389/fimmu.2024.1302909  \nCOPYRIGHT  \n© 2024 Yang, Zou, Wei, Zhang, Zhuo, Xu and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nIdentification and validation of biomarkers in membranous nephropathy and  \npan-cancer analysis  \nYue Yang, Gu-ming Zou, Xian-sen Wei, Zheng Zhang, Li Zhuo, Qian-qian Xu*† and Wen-ge Li*†  \nDepartment of Nephrology, China-Japan Friendship Hospital, Beijing, China  \nBackground: Membranous nephropathy (MN) is an autoimmune disease and represents the most prevalent type of renal pathology in adult patients afflicted with nephrotic syndrome. Despite substantial evidence suggesting a possible link between MN and cancer, the precise underlying mechanisms remain elusive.  \nMethods: In this study, we acquired and integrated two MN datasets (comprising a single-cell dataset and a bulk RNA-seq dataset) from the Gene Expression Omnibus database for differential expression gene (DEG) analysis, hub genes were obtained by LASSO and random forest algorithms, the diagnostic ability of hub genes was assessed using ROC curves, and the degree of immune cell infiltration was evaluated using the ssGSEA function. Concurrently, we gathered pan-cancerrelated genes from the TCGA and GTEx databases, to analyze the expression, mutation status, drug sensitivity and prognosis of hub genes in pan-cancer.  \nResults: We conducted intersections between the set of 318 senescence-related genes and the 366 DEGs, resulting in the identification of 13 senescence-related DEGs. Afterwards, we meticulously analyzed these genes using the LASSO and random forest algorithms, which ultimately led to the discovery of six hub genes through intersection (PIK3R1, CCND1, TERF2IP, SLC25A4, CAPN2, and TXN) . ROC curves suggest that these hub genes have good recognition of MN. After performing correlation analysis, examining immune infiltration, and conducting a comprehensive pan-cancer investigation, we validated these six hub genes through immunohistochemical analysis using human renal biopsy tissues. The pan-cancer analysis notably accentuates the robust association between these hub genes and the prognoses of individuals afflicted by diverse cancer types, further underscoring the importance of mutations within these hub genes across various cancers.  \nConclusion: This evidence indicates that these genes could potentially play a pivotal role as a critical link connecting MN and cancer. As a result, they may hold promise as valuable targets for intervention in cases of both MN and cancer.  \nKEYWORDS  \nmembranous nephropathy, pan-cancer, differential expression gene, potential link, biomarkers  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \n1 Introduction  \nMembranous nephropathy (MN) encompasses a","cbCairKlLdJBIusZ","https://ap.wps.com/l/cbCairKlLdJBIusZ","pdf",8141566,13,"English","# Introduction\n## Disease background and MN–cancer association\n## Gene expression profiling and biomarker discovery\n# Methods\n## MN dataset integration and differential expression\n## Hub gene identification and diagnostic evaluation\n## Immune infiltration estimation\n## Pan-cancer analyses\n## Experimental validation\n# Results\n## Senescence-related DEG intersection and hub gene discovery\n## ROC-based recognition of MN\n## Correlation, immune infiltration, and pan-cancer association\n# Conclusion","[{\"question\":\"What datasets were used to identify biomarkers for membranous nephropathy (MN)?\",\"answer\":\"Two MN datasets were acquired and integrated: a single-cell dataset and a bulk RNA-seq dataset from the Gene Expression Omnibus database for differential expression and downstream analysis.\"},{\"question\":\"How were hub genes for MN selected and evaluated diagnostically?\",\"answer\":\"Hub genes were obtained using LASSO and random forest algorithms. Their diagnostic ability was assessed with ROC curves.\"},{\"question\":\"How were the identified hub genes validated in this study?\",\"answer\":\"The six hub genes were validated through immunohistochemical analysis using human renal biopsy tissues, after computational pan-cancer and immune infiltration analyses.\"}]","Identification and validation of biomarkers in membranous nephropathy and pan-cancer analysis | PDF",1789996898,33]