[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122002-en":3,"doc-seo-122002-105":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},122002,5909877438554,"Maeve","https://ap-avatar.wpscdn.com/avatar/5600025385ad2bf12a7?_k=1778553567797529272",8,"Research & Report","Deciphering Breast Cancer Prognosis - A Novel Machine Learning-driven Model for Vascular Mimicry Signature Prediction","This original research develops an integrative machine learning framework to decode breast cancer prognosis through a vascular mimicry (VM) signature. VM describes tumor cells forming vasculogenic-like networks without endothelial cells, linked to aggressive behavior and reduced response to standard therapies. Using more than 6,000 patients across 12 datasets and additional single-cell data, the study evaluates 100 existing signatures via 108 model combinations, then validates findings with immunohistochemistry and tests therapeutic response patterns.","TYPE Original Research PUBLISHED 06 August 2024  \nDOI 10.3389/fimmu.2024.1414450  \nOPEN ACCESS  \nEDITED BY Xi Cheng,  \nShanghai Jiao Tong University, China  \nREVIEWED BY Zheng Yuan,  \nChina Academy of Chinese Medical Sciences, China  \nNicole Salazar,  \nNorth Carolina Central University, United States  \n*CORRESPONDENCE Tao Wang  \n[wangtaogpph@gzu.edu.cn](wangtaogpph@gzu.edu.cn)  \nRECEIVED 08 April 2024  \nACCEPTED 23 July 2024  \nPUBLISHED 06 August 2024  \nCITATION  \nLi X, Li X, Yang B, Sun S, Wang S, Yu Fand Wang T (2024) Deciphering breast cancer prognosis: a novel machine learning-driven model for vascular mimicry signature prediction.  \nFront. Immunol. 15:1414450 .  \ndoi: 10.3389/fimmu.2024.1414450  \nCOPYRIGHT  \n© 2024 Li, Li, Yang, Sun, Wang, Yu and Wang. 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.  \nDeciphering breast cancer prognosis: a novel machine learning-driven model for vascular mimicry signature prediction  \nXue Li 1,2, Xukui Li 1,2, Bin Yang 1,2, Songyang Sun 1,2, Shu Wang 3, Fuxun Yu 1,2 and Tao Wang 1,2*  \n1 Research Laboratory Center, Guizhou Provincial People’s Hospital, Guiyang, Guizhou, China, 2 NHC Key Laboratory of Pulmonary Immune-related Diseases, Guizhou Provincial People’s Hospital, Guizhou University, Guiyang, Guizhou, China, 3 Department of Breast Surgery, Guizhou Provincial People’s Hospital, Guiyang, Guizhou, China  \nBackground: In the ongoing battle against breast cancer, a leading cause of cancerrelated mortality among women globally, the urgent need for innovative prognostic markers and therapeutic targets is undeniable. This study pioneers an advanced methodology by integrating machine learning techniques to unveil a vascular mimicry signature, offering predictive insights into breast cancer outcomes. Vascular mimicry refers to the phenomenon where cancer cells mimic bloodvessel formation absent of endothelial cells, a trait associated with heightened tumor aggression and diminished response to conventional treatments.  \nMethods: The study’s comprehensive analysis spanned data from over 6,000 breast cancer patients across 12 distinct datasets, incorporating both proprietary clinical data and single-cell data from 7 patients, accounting for a total of 43,095 cells. By employing an integrative strategy that utilized 10 machine learning algorithms across 108 unique combinations, the research scrutinized 100 existing breast cancer signatures. Empirical validation was sought through immuno histochemistry assays, alongside explorations into potential immunotherapeutic and chemotherapeutic avenues.  \nResults: The investigation successfully identified six genes related to vascular mimicry from multi-center cohorts, laying the groundwork for a novel predictive model. This model outstripped the prognostic accuracy of traditional clinical and molecular indicators in forecasting recurrence and mortality risks. High-risk individuals identified by our model faced worse outcomes. Further validation through IHC assays in 30 patients underscored the model ’ s extensive applicability. Notably, the model unveiled varying therapeutic responses; lowrisk patients might achieve greater benefits from immunotherapy, whereas highrisk patients demonstrated a particular sensitivity to certain chemotherapies, such as ispinesib.  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nConclusions: This model marks a significant step forward in the precise evaluation of breast cancer prognosis and therapeutic responses across different patient groups. It heralds the possibility of refining patient outcomes through tailored ","cbCaidkVUqMqmRXT","https://ap.wps.com/l/cbCaidkVUqMqmRXT","pdf",9851723,1,17,"English","en",105,"# Background\n## Vascular mimicry and prognosis\n# Methods\n## Data sources and machine learning strategy\n## Validation and therapy exploration\n# Results\n## VM-related gene signature\n## Prognostic performance and patient stratification\n# Conclusions\n## Clinical and therapeutic implications","[{\"question\":\"What biological concept underpins this study’s prognostic approach?\",\"answer\":\"The study focuses on vascular mimicry, where aggressive tumor cells mimic blood vessel formation without endothelial cells, contributing to growth, metastasis, and treatment resistance.\"},{\"question\":\"How was the machine learning model built and evaluated?\",\"answer\":\"It analyzed over 6,000 breast cancer patients from 12 datasets, integrated proprietary clinical data with single-cell data, and tested 10 machine learning algorithms across 108 combinations while assessing 100 existing breast cancer signatures.\"},{\"question\":\"How did the VM signature perform compared with traditional indicators?\",\"answer\":\"The identified model outperformed traditional clinical and molecular indicators in predicting recurrence and mortality risk, and high-risk individuals showed worse outcomes; immunohistochemistry validation in additional patients supported its applicability.\"}]","Deciphering Breast Cancer Prognosis - A Novel Machine Learning-driven Model for Vascular Mimicry Signature Prediction | PDF",1785808236,43,{"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},"deciphering-breast-cancer-prognosis-a-novel-machine-learning-driven-model-for-vascular-mimicry-signature-prediction","",{"@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/deciphering-breast-cancer-prognosis-a-novel-machine-learning-driven-model-for-vascular-mimicry-signature-prediction/122002/",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","2026-08-04",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},"What biological concept underpins this study’s prognostic approach?","Question",{"text":75,"@type":76},"The study focuses on vascular mimicry, where aggressive tumor cells mimic blood vessel formation without endothelial cells, contributing to growth, metastasis, and treatment resistance.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How was the machine learning model built and evaluated?",{"text":80,"@type":76},"It analyzed over 6,000 breast cancer patients from 12 datasets, integrated proprietary clinical data with single-cell data, and tested 10 machine learning algorithms across 108 combinations while assessing 100 existing breast cancer signatures.",{"name":82,"@type":73,"acceptedAnswer":83},"How did the VM signature perform compared with traditional indicators?",{"text":84,"@type":76},"The identified model outperformed traditional clinical and molecular indicators in predicting recurrence and mortality risk, and high-risk individuals showed worse outcomes; 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