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Differential expression and feature selection with DESeq2 and multiple filters informed training of logistic regression, decision tree, random forest, and SVM models. Results highlight downregulation of hsa-miR-10400-5p and strong performance for random forest, suggesting a miRNA liquid-biopsy signature with diagnostic potential.",{"@graph":14,"@context":77},[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/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/urinary-micrornas-for-the-non-invasive-diagnosis-of-endometriosis-identified-by-next-generation-sequencing-and-machine-learning-research-open-access/458714/",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/urinary-micrornas-for-the-non-invasive-diagnosis-of-endometriosis-identified-by-next-generation-sequencing-and-machine-learning-research-open-access/458714.png","ImageObject",300,407,{"name":42,"@type":43},"4398046744996","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-31","2026-09-30",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",7,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69,73],{"name":60,"@type":61,"acceptedAnswer":62},"Why are urinary microRNAs considered for non-invasive endometriosis diagnosis?","Question",{"text":63,"@type":64},"MicroRNAs are stable in body fluids, and the study targets urine-based biomarkers to support detection without relying on diagnostic laparoscopy.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were the machine learning models built in the study?",{"text":68,"@type":64},"Urine miRNA profiles were generated by next-generation sequencing, differential expression was assessed with DESeq2, features were selected using variance filtering, univariate analysis, mutual information, and RFE, and top miRNAs trained four classifiers.",{"name":70,"@type":61,"acceptedAnswer":71},"Which miRNA and model showed the strongest diagnostic performance?",{"text":72,"@type":64},"hsa-miR-10400-5p was significantly downregulated in endometriosis, and the random forest model achieved the best performance (AUC = 0.91; accuracy and F1-score = 0.81).",{"name":74,"@type":61,"acceptedAnswer":75},"What validation is needed before clinical use?",{"text":76,"@type":64},"The identified miRNA signature, especially the novel hsa-miR-10400-5p, requires validation in larger independent cohorts to confirm clinical utility and reduce diagnostic delays.","https://schema.org",{"og:url":32,"og:type":79,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":81,"canonical":32},"index,follow",{"doc_id":83,"site_id":7},458714,1790977397,{"code":4,"msg":86,"data":87},"success",[88,92,96,100,105,110,113,118,123,126,130],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},"Exam",70,"exam",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},5,"Comic",60,"comic",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},6,"Technology",50,"technology",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":111,"slug":112},40,"healthcare",{"id":114,"doc_module":4,"doc_module_name":25,"category_name":115,"show_sort_weight":116,"slug":117},8,"Research & Report",30,"research-report",{"id":119,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":121,"slug":122},9,"Religion & Spirituality",20,"religion-spirituality",{"id":121,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":121,"slug":125},"World Cup","world-cup",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":127,"slug":129},10,"Lifestyle","lifestyle",{"id":131,"doc_module":4,"doc_module_name":25,"category_name":132,"show_sort_weight":101,"slug":133},19,"General","general",{"code":4,"msg":86,"data":135},{"doc_id":83,"user_id":136,"nickname":42,"user_avatar":11,"doc_module":4,"category_id":55,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":137,"file_id":138,"file_url":139,"file_type":140,"file_size":141,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":119,"language":142,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":143,"faqs":144,"seo_title":145,"seo_description":12,"update_tm":146,"read_time":147,"preview_page_count":4},4398046744996,"Kupec etal. Reproductive Biology and Endocrinology (2026) 24:4  \n[https://doi.org/10.1186/s12958-025-01517-6](https://doi.org/10.1186/s12958-025-01517-6)  \nReproductive Biology and Endocrinology  \nRESEARCH Open Access  \nUrinary microRNAs for the non-invasive diagnosis of endometriosis identified by nextgeneration sequencing and machine learning  \nTomas Kupec1*, Julia Wittenborn1, Chao-Chung Kuo2, Laila Najjari1, Rebecca Senger1, Philipp Meyer-Wilmes1, Elmar Stickeler1 and Jochen Maurer 1  \nAbstract  \nBackground Endometriosis is a chronic gynecological disease associated with pain, infertility, and delayed diagnosis. Non-invasive biomarkers are urgently needed to facilitate earlier detection and reduce the reliance on diagnostic laparoscopy. MicroRNAs (miRNAs) are stable in body fluids and hold promise as diagnostic tools.  \nMethods In this pilot study, urine samples from 34 patients with histologically confirmed endometriosis and 18 control patients (laparoscopically confirmed absence of disease) were analyzed using next-generation miRNA sequencing. Differential expression analysis was performed with DESeq2 . Feature selection applied variance filtering, univariate analysis, mutual information, and recursive feature elimination (RFE) . The top 20 miRNAs were used to train four classification models: logistic regression, decision tree, random forest, and support vector machine (SVM) . Model performance was evaluated by accuracy, precision, recall, F1-score, and area under the ROC curve (AUC) .  \nResults Among all detected miRNAs, hsa-miR-10400-5p was significantly downregulated in endometriosis compared to controls (log₂ fold change − 2 . 70; adjusted p = 0 . 015) . RFE identified 20 miRNAs, including hsa-mir-183, hsa-mir-500a, hsa-miR-3184-5p, hsa-miR-151b, and hsa-mir-196a-1, as the most informative for classification. The random forest model achieved the best performance (AUC = 0 . 91; accuracy and F1-score = 0 . 81), outperforming logistic regression (AUC = 0 . 83) and SVM (AUC = 0 . 81) . Several identified miRNAs have been previously implicated in endometriosis pathogenesis, and we additionally identified hsa-miR-10400-5p as a significantly downregulated and previously unreported biomarker candidate, representing a novel finding with potential diagnostic relevance. Conclusions Urinary miRNA profiling, combined with machine learning, shows promise as a completely noninvasive approach for the diagnosis of endometriosis. The identified miRNA signature, particularly the novel hsa-miR- 10400-5p, warrants validation in larger, independent cohorts to confirm its clinical utility and potential to reduce diagnostic delays.  \nKeywords Endometriosis, Chronic pelvic pain, Urine, miRNA, Liquid biopsy, Biomarker, RNA-based diagnostics  \n*Correspondence: Tomas Kupec[tkupec@ukaachen.de](tkupec@ukaachen.de)  \n1Department of Gynecology and Obstetrics, University Hospital RWTH Aachen, Aachen, Germany  \n2Genomics Facility, Interdisciplinary Center for Clinical Research (IZKF), University Hospital RWTH Aachen, Aachen, Germany  \n© The Author(s) 2026. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/.)[.](http://cr","cbCaijef3IjfJzZ4","https://ap.wps.com/l/cbCaijef3IjfJzZ4","pdf",1424179,"English","# Abstract\n# Introduction\n# Materials and Methods\n## Sequencing and Differential Expression\n## Feature Selection and Model Training\n# Results\n## Differential Expression Findings\n## Classification Performance\n# Conclusions","[{\"question\":\"Why are urinary microRNAs considered for non-invasive endometriosis diagnosis?\",\"answer\":\"MicroRNAs are stable in body fluids, and the study targets urine-based biomarkers to support detection without relying on diagnostic laparoscopy.\"},{\"question\":\"How were the machine learning models built in the study?\",\"answer\":\"Urine miRNA profiles were generated by next-generation sequencing, differential expression was assessed with DESeq2, features were selected using variance filtering, univariate analysis, mutual information, and RFE, and top miRNAs trained four classifiers.\"},{\"question\":\"Which miRNA and model showed the strongest diagnostic performance?\",\"answer\":\"hsa-miR-10400-5p was significantly downregulated in endometriosis, and the random forest model achieved the best performance (AUC = 0.91; accuracy and F1-score = 0.81).\"},{\"question\":\"What validation is needed before clinical use?\",\"answer\":\"The identified miRNA signature, especially the novel hsa-miR-10400-5p, requires validation in larger independent cohorts to confirm clinical utility and reduce diagnostic delays.\"}]","Urinary microRNAs for the non-invasive diagnosis of endometriosis identified by next-generation sequencing and machine learning - Research Open Access | PDF",1790754166,23]