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Machine learning identified discriminatory proteins and diagnostic panels. Cervico-vaginal fluid signatures outperformed plasma, supporting proof-of-principle protein biomarkers. Confirmation in larger independent cohorts is required.",{"@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/healthcare/","Healthcare",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/detection-of-endometrial-cancer-in-cervico-vaginal-fluid-and-blood-plasma-leveraging-proteomics-and-machine-learning-for-biomarker-discovery/330290/",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/detection-of-endometrial-cancer-in-cervico-vaginal-fluid-and-blood-plasma-leveraging-proteomics-and-machine-learning-for-biomarker-discovery/330290.png","ImageObject",300,407,{"name":42,"@type":43},"Finn","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-23","2026-09-21",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"Why are cervico-vaginal fluid and blood plasma useful for endometrial cancer detection?","Question",{"text":63,"@type":64},"The uterine cavity and lower genital tract are anatomically continuous, allowing tumor-derived biomaterials to be collected non-invasively. Plasma is also practical because it is easy to collect, though cancer signals in blood can be limited.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were the proteomic biomarkers discovered in this study?",{"text":68,"@type":64},"Proteomic maps were generated for each sample using SWATH-MS. Machine learning then identified proteins with the highest discriminatory value between cancer cases and controls, and the best diagnostic model was selected based on accuracy and parsimony.",{"name":70,"@type":61,"acceptedAnswer":71},"Which biomarker panels performed best, and what diagnostic performance was reported?",{"text":72,"@type":64},"A 5-biomarker panel from cervico-vaginal fluid achieved AUC 0.95 with sensitivity 91% and specificity 86%. A 3-marker plasma panel achieved AUC 0.87 with sensitivity 75% and specificity 84%.","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},330290,1790076655,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,110,115,120,123,126],{"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":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":108,"slug":109},7,40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":112,"show_sort_weight":113,"slug":114},8,"Research & Report",30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":55,"slug":125},"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":107,"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},34359740700684,"https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487","Articles   \nDetection of endometrial cancer in cervico-vaginal fluid and blood plasma: leveraging proteomicsand machine learning for biomarker discovery  \nKelechi Njoku,a,b,c,∗∗ Andrew Pierce,d Davide Chiasserini,e Bethany Geary,f Amy E. Campbell,b Janet Kelsall,b Rachel Reed,b Nophar Geifman,g Anthony D. Whetton,h,i and EmmaJ. Crosbiea,i,∗  \naDivision of Cancer Sciences, University of Manchester, School of Medical Sciences, Faculty of Biology, Medicine and Health, 5th Floor Research, St Mary’s Hospital, Road, Manchester, M13 9WL, UK  \nbStoller Biomarker Discovery Centre, Institute of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK  \ncDepartment of Clinical Oncology, Christie NHS Foundation Trust, Manchester, UK dNorth Wales Medical School, Bangor University, Bangor, Gwynedd, LL57 2DG, UK  \neDepartment of Medicine and Surgery, Section of Physiology and Biochemistry, University of Perugia, 06132, Perugia, Italy fMedical Research Council Protein Phosphorylation and Ubiquitylation Unit, School of Life Sciences, University of Dundee, Dow Street, Dundee, DD1 5EH, UK  \ngSchool of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH, UK  \nhVeterinary Health Innovation Engine, School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, GU2 7XH, UK  \nSummary  \nBackground The anatomical continuity between the uterine cavity and the lower genital tract allows for the exploitation of uterine-derived biomaterial in cervico-vaginal fluid for endometrial cancer detection based on non-invasive sampling methodologies. Plasma is an attractive biofluid for cancer detection due to its simplicity and ease of collection. In this biomarker discovery study, we aimed to identify proteomic signatures that accurately discriminate endometrial cancer from controls in cervico-vaginal fluid and blood plasma.  \nMethods Blood plasma and Delphi Screener-collected cervico-vaginal fluid samples were acquired from symptomatic post-menopausal women with (n = 53) and without (n = 65) endometrial cancer. Digitised proteomic maps were derived for each sample using sequential window acquisition of all theoretical mass spectra (SWATH-MS) . Machine learning was employed to identify the most discriminatory proteins. The best diagnostic model was determined based on accuracy and model parsimony.  \nFindings A protein signature derived from cervico-vaginal fluid more accurately discriminated cancer from control samples than one derived from plasma. A 5-biomarker panel of cervico-vaginal fluid derived proteins (HPT, LG3BP, FGA, LY6D and IGHM) predicted endometrial cancer with an AUC of 0.95 (0.91–0.98), sensitivity of 91%(83%–98%), and specificity of 86%(78%–95%) . By contrast, a 3-marker panel of plasma proteins (APOD, PSMA7 and HPT) predicted endometrial cancer with an AUC of 0.87 (0.81–0.93), sensitivity of 75%(64%– 86%), and specificity of 84% (75%–93%) . The parsimonious model AUC values for detection of stage I endometrial cancer in cervico-vaginal fluid and blood plasma were 0.92 (0.87–0.97) and 0.88 (0.82–0.95) respectively.  \nInterpretation Here, we leveraged the natural shed of endometrial tumours to potentially develop an innovative approach to endometrial cancer detection. We show proof of principle that endometrial cancers secrete unique protein signatures that can enable cancer detection via cervico-vaginal fluid assays. Confirmation in a larger independent cohort is warranted.  \nFunding Cancer Research UK, Blood Cancer UK, National Institute for Health Research.  \n*Corresponding author. Division of Cancer Sciences, University of Manchester, School of Medical Sciences, Faculty of Biology, Medicine and Health, 5th Floor Research, St Mary’s Hospital, Oxford Road, Manchester, M13 9PL, UK.  \n**Corresponding author.  \nE-mail addresses: [emma.crosbie@manchester.ac.uk](emma.crosbie@manchester.ac.uk) (E.J. Crosbie), [kelechi.njoku@manchester.ac.uk](kelechi.njoku@ma","cbCaiv4lJZpEI4YT","https://ap.wps.com/l/cbCaiv4lJZpEI4YT","pdf",1768813,13,"English","# Background\n# Methods\n# Findings\n# Interpretation\n# Funding and Keywords","[{\"question\":\"Why are cervico-vaginal fluid and blood plasma useful for endometrial cancer detection?\",\"answer\":\"The uterine cavity and lower genital tract are anatomically continuous, allowing tumor-derived biomaterials to be collected non-invasively. Plasma is also practical because it is easy to collect, though cancer signals in blood can be limited.\"},{\"question\":\"How were the proteomic biomarkers discovered in this study?\",\"answer\":\"Proteomic maps were generated for each sample using SWATH-MS. Machine learning then identified proteins with the highest discriminatory value between cancer cases and controls, and the best diagnostic model was selected based on accuracy and parsimony.\"},{\"question\":\"Which biomarker panels performed best, and what diagnostic performance was reported?\",\"answer\":\"A 5-biomarker panel from cervico-vaginal fluid achieved AUC 0.95 with sensitivity 91% and specificity 86%. A 3-marker plasma panel achieved AUC 0.87 with sensitivity 75% and specificity 84%.\"}]","Detection of endometrial cancer in cervico-vaginal fluid and blood plasma - leveraging proteomics and machine learning for biomarker discovery | PDF",1789988029,33]