[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127539-en":3,"doc-seo-127539-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127539,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms","This original research examines potential biological targets and shared mechanisms linking SARS-CoV-2 infection with benign prostatic hyperplasia (BPH) and its related symptoms. Public GEO expression datasets for COVID-19 and BPH were analyzed to derive common differentially expressed genes, followed by PPI, GO enrichment, and KEGG pathway mapping. Three machine-learning approaches identified five hub genes, validated in independent datasets, and CIBERSORT supported immune-cell associations. The study further proposes drug candidates and small molecules for therapeutic exploration.","TYPE Original Research PUBLISHED 23 June 2023  \nDOI 10.3389/fimmu.2023.1172724  \nOPEN ACCESS  \nEDITED BY  \nWilliam Tolbert,  \nHenry M Jackson Foundation for the Advancement of Military Medicine (HJF), United States  \nREVIEWED BY  \nLinda Vignozzi,  \nUniversity of Florence, Italy Yupeng Wu,  \nFirst Affiliated Hospital of Fujian Medical University, China  \nYuxuan Song,  \nPeking University People ’s Hospital, China  \n*CORRESPONDENCE Xiaoqiang Liu  \n [xiaoqiangliu1@163.com](xiaoqiangliu1@163.com)  \n†These authors have contributed equally to this work  \nRECEIVED 23 February 2023  \nACCEPTED 07 June 2023  \nPUBLISHED 23 June 2023  \nCITATION  \nZhou H, Xu M, Hu P, Li Y, Ren C, Li M, Pan Y, Wang S and Liu X (2023) Identifying hub genes and common biological  \npathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms.  \nFront. Immunol. 14:1172724 .  \ndoi: 10.3389/fimmu.2023.1172724  \nCOPYRIGHT  \n© 2023 Zhou, Xu, Hu, Li, Ren, Li, Pan, Wang and Liu. 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.  \nIdentifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms  \nHang Zhou 1†, Mingming Xu 1†, Ping Hu 2†, Yuezheng Li 1, Congzhe Ren 1, Muwei Li 1, Yang Pan 1, Shangren Wang 1 and Xiaoqiang Liu 1*  \n1 Department of Urology, Tianjin Medical University General Hospital, Tianjin, China, 2 Department of Orthopedics, Tianjin Medical University General Hospital, Tianjin, China  \nBackground: COVID-19, a serious respiratory disease that has the potential to affect numerous organs, is a serious threat to the health of people around the world. The objective of this article is to investigate the potential biological targetsand mechanisms by which SARS-CoV-2 affects benign prostatic hyperplasia (BPH) and related symptoms.  \nMethods: We downloaded the COVID-19 datasets (GSE157103 and GSE166253) and the BPH datasets (GSE7307 and GSE132714) from the Gene Expression Omnibus (GEO) database. In GSE157103 and GSE7307, differentially expressed genes (DEGs) were found using the “Limma” package, and the intersection was utilized to obtain common DEGs. Further analyses followed, including those using Protein-Protein Interaction (PPI), Gene Ontology (GO) function enrichment analysis, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) . Potential hub genes were screened using three machine learning methods, and they were later verified using GSE132714 and GSE166253 . The CIBERSORT analysis and the identification of transcription factors, miRNAs, and drugs as candidates were among the subsequent analyses.  \nResults: We identified 97 common DEGs from GSE157103 and GSE7307 . According to the GO and KEGG analyses, the primary gene enrichment pathways were immune-related pathways. Machine learning methods were used to identify five hub genes (BIRC5, DNAJC4, DTL, LILRB2, and NDC80) . They had good diagnostic properties in the training sets and were validated in the validation sets. According to CIBERSORT analysis, hub genes were closely related to CD4 memory activated of T cells, T cells regulatory and NK cells activated. The top 10 drug candidates (lucanthone, phytoestrogens, etoposide, dasatinib, piroxicam, pyrvinium, rapamycin, niclosamide, genistein, and testosterone) will also be evaluated by the P value, which is expected to be helpful for the treatment of COVID-19-infected patients with BPH.  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nConclusion: Our findings reveal common signaling pathways, possible biological targets, and promising small molecule drugs","cbCail9OQDCminOI","https://ap.wps.com/l/cbCail9OQDCminOI","pdf",10607770,2,1,14,"English","en",105,"# Background\n# Methods\n# Results\n# Conclusion\n# Keywords\n# Introduction","[{\"question\":\"本文的研究目的是什么？\",\"answer\":\"研究旨在探讨SARS-CoV-2影响BPH及相关症状时可能存在的生物学靶点与共同机制。\"},{\"question\":\"研究使用了哪些数据与方法来筛选关键基因？\",\"answer\":\"从GEO获取COVID-19与BPH表达数据，使用Limma筛选差异基因并取交集；随后结合PPI、GO与KEGG富集分析，并用三种机器学习方法筛选枢纽基因。\"},{\"question\":\"枢纽基因与免疫细胞的关系如何验证？\",\"answer\":\"通过CIBERSORT分析，结果显示枢纽基因与CD4记忆活化T细胞、T细胞调节及被活化的NK细胞密切相关，并在独立数据集中进行验证。\"}]","Identifying hub genes and common biological pathways between COVID-19 and benign prostatic hyperplasia by machine learning algorithms | PDF",1785939842,35,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"identifying-hub-genes-and-common-biological-pathways-between-covid-19-and-benign-prostatic-hyperplasia-by-machine-learning-algorithms","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/identifying-hub-genes-and-common-biological-pathways-between-covid-19-and-benign-prostatic-hyperplasia-by-machine-learning-algorithms/127539/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"本文的研究目的是什么？","Question",{"text":76,"@type":77},"研究旨在探讨SARS-CoV-2影响BPH及相关症状时可能存在的生物学靶点与共同机制。","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"研究使用了哪些数据与方法来筛选关键基因？",{"text":81,"@type":77},"从GEO获取COVID-19与BPH表达数据，使用Limma筛选差异基因并取交集；随后结合PPI、GO与KEGG富集分析，并用三种机器学习方法筛选枢纽基因。",{"name":83,"@type":74,"acceptedAnswer":84},"枢纽基因与免疫细胞的关系如何验证？",{"text":85,"@type":77},"通过CIBERSORT分析，结果显示枢纽基因与CD4记忆活化T细胞、T细胞调节及被活化的NK细胞密切相关，并在独立数据集中进行验证。","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]