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Prognostic implications of palmitoylation remain unclear. This study identifies palmitoylation-related prognostic factors in breast cancer and evaluates their ability to predict chemotherapy and immunotherapy responses. Differential expression and hub genes are derived from limma and WGCNA, followed by LASSO Cox model building, nomogram creation, and response assessment using immunophenoscore and pRophetic.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/insights-into-a-machine-learning-based-palmitoylation-related-gene-model-for-predicting-the-prognosis-and-treatment-response-of-breast-cancer-patients/332413/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/insights-into-a-machine-learning-based-palmitoylation-related-gene-model-for-predicting-the-prognosis-and-treatment-response-of-breast-cancer-patients/332413.png","ImageObject",300,407,{"name":92,"@type":93},"Asher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-28","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main goal of the study?","Question",{"text":112,"@type":113},"To develop a machine learning-based gene model related to palmitoylation that predicts prognosis and treatment response in breast cancer patients.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which methods are used to construct the prognostic model?",{"text":117,"@type":113},"Differential gene expression is analyzed using limma, hub genes are identified using WGCNA, and a prognostic feature and nomogram are built using LASSO Cox regression and regplot.",{"name":119,"@type":110,"acceptedAnswer":120},"How are patients classified and what differences are observed?",{"text":121,"@type":113},"Patients are divided into high-risk and low-risk groups by median risk score; high-risk patients show lower survival, while low-risk patients show greater immune cell infiltration and better chemotherapy and immunotherapy responses.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},332413,1790587521,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":36,"preview_page_count":4},687197207639,"https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd","Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients  \nHongxia Zhu, MS 1,2,\\#, Haihong Hu, MS 1,2,\\#, Bo Hao, BD2, Wendi Zhan, MS 1,2, Ting Yan, MS3, Jingdi Zhang, MS 1,2, Siyu Wang, MS4, Hongjuan Hu, MS5, and Taolan Zhang, MD2,6  \nTechnology in Cancer Research & Treatment  \nVolume 23: 1-16  \n© The Author(s) 2024  \nArticle reuse guidelines: [sagepub.com/journals-permissions](sagepub.com/journals-permissions)[ ](sagepub.com/journals-permissions)[DOI: 10.1177/15330338241263434](DOI: 10.1177/15330338241263434)[ ](DOI: 10.1177/15330338241263434)[journals.sagepub.com/home/tct](journals.sagepub.com/home/tct)  \nAbstract  \nBackground: Breast cancer is a prevalent public health concern affecting numerous women globally and is associated with palmitoylation, a post-translational protein modification. Despite increasing focus on palmitoylation, its specific implications for breast cancer prognosis remain unclear. The work aimed to identify prognostic factors linked to palmitoylation in breast cancer and assess its effectiveness in predicting responses to chemotherapy and immunotherapy. Methods: We utilized the “limma”package to analyze the differential expression of palmitoylation-related genes between breast cancer and normal tissues. Hub genes were identified using the “WGCNA” package. Using the least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a prognostic feature associated with palmitoylation and developed a prognostic nomogram with the “regplot” package. The predictive values of the model for chemotherapy and immunotherapy responses were assessed using immunophenoscore (IPS) and the “pRophetic” package. Results: We identified 211 differentially expressed genes related topalmitoylation, among which 44 demonstrated prognostic potential. Subsequently, a predictive model comprising eleven palmitoylation-related genes was developed. Patients were classified into high-risk and low-risk groups based on the median risk score. The findings revealed that individuals in the high-risk group exhibited lower survival rates, while those in the low-risk group showed increased immune cell infiltration and improved responses to chemotherapy and immunotherapy. Moreover, the BC-Palmitoylation Tool website was established. Conclusion: This study developed the first machine learning-based predictive model for palmitoylation-related genes and created a corresponding website, providing clinicians with a valuable tool to improve patient outcomes.  \nKeywords  \nbreast cancer, palmitoylation, prognostic model, nomogram, website application  \nReceived: March 7, 2024; Revised: May 10, 2024; Accepted: May 30, 2024.  \n1 School of Pharmacy, Hengyang Medical College, University of South China, Hengyang, China  \n2 Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China  \n3 Department of Breast and Thyroid Surgery, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China  \n4 Department of Medical Oncology, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China  \n5 Department of Public Health Service, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China  \n6 Phase I Clinical Trial Center, The First Affiliated Hospital, Hengyang Medical School, University of South China, Hengyang, China  \n\\# These authors contributed equally to this work.  \nCorresponding Author:  \nTaolan Zhang, Department of Pharmacy, The First Affiliated Hospital, Hengyang Medical School, University of South China, 69 Chuanshan Road, Hengyang, Hunan 421000, China.  \n[Email: TaolZhan@usc.edu.cn](Email: TaolZhan@usc.edu.cn)  \nCreative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License ([https://cr","cbCaiq3wTyj1o7BY","https://ap.wps.com/l/cbCaiq3wTyj1o7BY","pdf",5850174,16,"English","# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Introduction","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To develop a machine learning-based gene model related to palmitoylation that predicts prognosis and treatment response in breast cancer patients.\"},{\"question\":\"Which methods are used to construct the prognostic model?\",\"answer\":\"Differential gene expression is analyzed using limma, hub genes are identified using WGCNA, and a prognostic feature and nomogram are built using LASSO Cox regression and regplot.\"},{\"question\":\"How are patients classified and what differences are observed?\",\"answer\":\"Patients are divided into high-risk and low-risk groups by median risk score; high-risk patients show lower survival, while low-risk patients show greater immune cell infiltration and better chemotherapy and immunotherapy responses.\"}]","Insights into a Machine Learning-Based Palmitoylation-Related Gene Model for Predicting the Prognosis and Treatment Response of Breast Cancer Patients | PDF",1789998113]