[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118981-en":3,"doc-seo-118981-105":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},118981,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning-Based Classification of Transcriptome Signatures of Non-Ulcerative Bladder Pain Syndrome","Lower urinary tract dysfunction (LUTD) creates a major health burden through symptoms that overlap across related disorders, while the lack of reliable biomarkers limits accurate subtype classification. This study presents a machine learning framework to identify mRNA signatures specific to non-ulcerative bladder pain syndrome (BPS). Using next-generation sequencing transcriptome data from bladder biopsies, candidate genes were discovered and then validated with QPCR. Supervised and unsupervised ML on the QPCR cohort yielded a three-mRNA signature (TPPP3, FAT1, NCALD) as a robust BPS classifier. The resulting ML pipeline addresses small-sample challenges and supports broader signature discovery.","source: [https://doi.org/10.48350/192765 | downloaded:](https://doi.org/10.48350/192765 | downloaded:) 4.6.2024  \nInternational Journal of  \nMolecular Sciences  \nArticle  \nMachine Learning-Based Classification of Transcriptome Signatures of Non-Ulcerative Bladder Pain Syndrome  \nAkshay Akshay 1,2, Mustafa Besic 1, Annette Kuhn 3, Fiona C. Burkhard 1,4, Alex Bigger-Allen 5,6,7, Rosalyn M. Adam 5,6,7, Katia Monastyrskaya 1,4,* and Ali Hashemi Gheinani 1,4,5,6,7, *  \nCitation: Akshay, A.; Besic, M.; Kuhn, A.; Burkhard, F.C.; Bigger-Allen, A.; Adam, R.M.; Monastyrskaya, K.; Hashemi Gheinani, A. Machine Learning-Based Classification of Transcriptome Signatures of  \nNon-Ulcerative Bladder Pain Syndrome. Int. J. Mol. Sci. 2024, 25, 1568. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ijms25031568  \nAcademic Editor: Ramin Ranjbarzadeh  \nReceived: 21 December 2023  \nRevised: 19 January 2024  \nAccepted: 21 January 2024  \nPublished: 26 January 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Functional Urology Research Laboratory, Department for BioMedical Research DBMR, University of Bern,  \n3008 Bern, Switzerland; [akshay.akshay@unibe.ch](akshay.akshay@unibe.ch) (A.A.); [mustafa.besic@unibe.ch](mustafa.besic@unibe.ch) (M.B.);  \n[fiona.burkhard@insel.ch](fiona.burkhard@insel.ch) (F.C.B.)  \n2 Graduate School for Cellular and Biomedical Sciences, University of Bern, 3012 Bern, Switzerland  \n3 Department of Gynaecology, Inselspital University Hospital, 3010 Bern, Switzerland; [annette.kuhn@insel.ch](annette.kuhn@insel.ch)  \n4 Department of Urology, Inselspital University Hospital, University of Bern, 3012 Bern, Switzerland  \n5 Urological Diseases Research Center, Boston Children’s Hospital, Boston, MA 02115, USA; [aab589@g.harvard.edu](aab589@g.harvard.edu) (A.B.-A.); [rosalyn.adam@childrens.harvard.edu](rosalyn.adam@childrens.harvard.edu) (R.M.A.)  \n6 Department of Surgery, Harvard Medical School, Boston, MA 02114, USA  \n7 Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA  \n* Correspondence: [katia.monastyrskaia@unibe.ch](katia.monastyrskaia@unibe.ch) (K.M.); [ali.hashemi@unibe.ch](ali.hashemi@unibe.ch) (A.H.G.); Tel.: +41-3163-28776 (K.M.); +41-3163-20981 (A.H.G.)  \nAbstract: Lower urinary tract dysfunction (LUTD) presents a global health challenge with symptoms impacting a substantial percentage of the population. The absence of reliable biomarkers complicates the accurate classification of LUTD subtypes with shared symptoms such as non-ulcerative Bladder Pain Syndrome (BPS) and overactive bladder caused by bladder outlet obstruction with Detrusor Overactivity (DO). This study introduces a machine learning (ML)-based approach for the identification of mRNA signatures specific to non-ulcerative BPS. Using next-generation sequencing (NGS) transcriptome data from bladder biopsies of patients with BPS, benign prostatic obstruction with DO, and controls, our statistical approach successfully identified 13 candidate genes capable of discerning BPS from control and DO patients. This set was validated using Quantitative Polymerase Chain Reaction (QPCR) in a larger patient cohort. To confirm our findings, we applied both supervised and unsupervised ML approaches to the QPCR dataset. A three-mRNA signature TPPP3, FAT1, and NCALD, emerged as a robust classifier for non-ulcerative BPS. The ML-based framework used to define BPS classifiers establishes a solid foundation for comprehending the gene expression changes in the bladder during BPS and serves as a valuable resource and methodology for advancing signature identification in other fields. The proposed ML pipeline demonstrates its effic","cbCailStDlKO0hQ0","https://ap.wps.com/l/cbCailStDlKO0hQ0","pdf",8565302,1,20,"English","en",105,"# Introduction\n## Lower urinary tract dysfunction and clinical burden\n## Symptom overlap between BPS and overactive bladder\n## Role of bladder outlet obstruction and detrusor overactivity\n## Rationale for genetic biomarkers and transcriptome-based approaches","[{\"question\":\"Why is biomarker-based classification for LUTD subtypes challenging?\",\"answer\":\"LUTD subtypes share overlapping symptoms, and reliable biomarkers are currently unavailable. This complicates accurate differentiation among disorders such as non-ulcerative BPS and overactive bladder.\"},{\"question\":\"How were transcriptome data and mRNA signatures used in this study?\",\"answer\":\"The study used next-generation sequencing transcriptome data from bladder biopsies to identify candidate genes differentiating BPS from control and DO patients. The candidates were then validated using QPCR.\"},{\"question\":\"What classifier signature emerged as most robust for non-ulcerative BPS?\",\"answer\":\"A three-mRNA signature—TPPP3, FAT1, and NCALD—was identified as a robust classifier for non-ulcerative BPS using supervised and unsupervised machine learning on the QPCR dataset.\"}]","Machine Learning-Based Classification of Transcriptome Signatures of Non-Ulcerative Bladder Pain Syndrome | PDF",1785721348,50,{"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},"machine-learning-based-classification-of-transcriptome-signatures-of-non-ulcerative-bladder-pain-syndrome","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-based-classification-of-transcriptome-signatures-of-non-ulcerative-bladder-pain-syndrome/118981/",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","2026-08-03",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},"Why is biomarker-based classification for LUTD subtypes challenging?","Question",{"text":75,"@type":76},"LUTD subtypes share overlapping symptoms, and reliable biomarkers are currently unavailable. This complicates accurate differentiation among disorders such as non-ulcerative BPS and overactive bladder.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were transcriptome data and mRNA signatures used in this study?",{"text":80,"@type":76},"The study used next-generation sequencing transcriptome data from bladder biopsies to identify candidate genes differentiating BPS from control and DO patients. The candidates were then validated using QPCR.",{"name":82,"@type":73,"acceptedAnswer":83},"What classifier signature emerged as most robust for non-ulcerative BPS?",{"text":84,"@type":76},"A three-mRNA signature—TPPP3, FAT1, and NCALD—was identified as a robust classifier for non-ulcerative BPS using supervised and unsupervised machine learning on the QPCR dataset.","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},[93,97,101,105,110,114,119,122,126,129,133],{"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":29,"slug":113},6,"Technology","technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":21,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":127,"show_sort_weight":21,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":46,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":46,"category_name":135,"show_sort_weight":106,"slug":136},19,"General","general"]