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This study addresses the limited availability of prognostic evidence for anoikis-related genes (ARGs) in breast cancer. Gene expression datasets from TCGA, GEO, and METABRIC were integrated to derive five ARG signatures using Cox regression and LASSO workflows, then build a risk score model. Patients were stratified into high- and low-risk groups, and associations were evaluated across overall survival, tumor mutation burden, tumor microenvironment features, stemness, and drug sensitivity, with RT-qPCR validation and a nomogram for clinical risk prediction.",{"@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/research-report/","Research & 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is the main goal of this study on anoikis-related genes in breast cancer?","Question",{"text":63,"@type":64},"To identify anoikis-related gene signatures that can predict prognosis and characterize immune microenvironment and drug sensitivity in breast cancer patients.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How were the gene signatures selected and the risk score model constructed?",{"text":68,"@type":64},"Five anoikis-related signatures were selected from ARGs using univariate Cox regression, LASSO regression, and multivariate Cox regression, followed by building an ARGs risk score model to stratify patients into high- and low-risk groups.",{"name":70,"@type":61,"acceptedAnswer":71},"Which patient and tumor characteristics were compared between high- and low-risk groups?",{"text":72,"@type":64},"Overall survival (OS), tumor mutation burden (TMB), tumor microenvironment (TME), stemness, and drug sensitivity were analyzed for associations with the risk 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Gene Signatures to Predict the Prognosis, Immune Microenvironment, and Drug Sensitivity of Breast Cancer Patients  \nCancer Control Volume 31: 1–18  \n© The Author(s) 2024  \nArticle reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)  \n[DOI: 10.1177/10732748241288118](DOI: 10.1177/10732748241288118)[ ](DOI: 10.1177/10732748241288118)[journals.sagepub.com/home/ccx](journals.sagepub.com/home/ccx)  \nJiena Liu, MD 1,* 􀀁, Hao Wu, MD2,3,*, Qin Wang, MD2,3,4,*, Shengye Jin, MD 1, Siyu Hou, MD 1, Zibo Shen, MD5, Liuying Zhao, MD 1, Shouping Xu, MD 1,2,3, and Da Pang, MD 1,3  \nAbstract  \nIntroduction: Breast cancer is one of the most prevalent types of cancer and a leading cause of cancer-related death among females worldwide. Anoikis, a specific type of apoptosis that is triggered by the loss of anchoring between cells and the native extracellular matrix, plays a vital role in cancer invasion and metastasis. However, studies that focus on the prognostic values of anoikis-related genes (ARGs) in breast cancer are scarce.  \nMethods: Gene expression data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases. Five anoikis-related signatures (ARS) were selected from ARGs through univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. Subsequently, an ARGs risk score model was established, and breast cancer patients were divided into high and low risk groups. The correlation between risk groups and overall survival (OS), tumor mutation burden (TMB), tumor microenvironment (TME), stemness, and drug sensitivity were analyzed. Moreover, RT-qPCR was performed to verify the gene expression levels of the five ARS in breast cancer tissues. Furthermore, a nomogram model was constructed based on ARGs risk score and clinicopathological factors.  \nResults: A novel ARGs risk score model was constructed based on five ARS (CEMIP, LAMB3, CD24, PTK6, and PLK1), and breast cancer patients were divided into high and low risk groups. Correlation analysis showed that the high and low risk groups had different OS, TMB, TME, stemness, and drug sensitivity. Both the ARGs risk score model and the nomogram showed promising prognosis predictive value in breast cancer.  \n1 Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin, China  \n2Key Laboratory of Tumor Biotherapy of Heilongjiang Province, Harbin Medical University Cancer Hospital, Harbin, China  \n3Translational Medicine Research and Cooperation Center of Northern China, Heilongjiang Academy of Medical Sciences, Harbin, China 4Department of Pharmacology (The State-Province Key Laboratories of Biomedicine-Pharmaceutics of China), College of Pharmacy of Harbin Medical University, Harbin, China  \n5Department of Biomedical and Life Science Faculty, King’s College London, London, UK  \n*The first authors: Jiena Liu, Hao Wu, and Qin Wang  \nCorresponding Authors:  \nDa Pang, Department of Breast Surgery, Harbin Medical University Cancer Hospital, 150 Haping Road, Harbin 150081, China.  \nEmail: [pangda@ems.hrbmu.edu.cn](pangda@ems.hrbmu.edu.cn)  \nShouping Xu, Department of Breast Surgery, Harbin Medical University Cancer Hospital, Harbin 150081, 150 Haping Road, China.  \nEmail: [Shoupingxu@hrbmu.edu.cn](Shoupingxu@hrbmu.edu.cn)  \nCreative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons  \nAttribution-NonCommercial 4.0 License ([https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)) which permits non-commercial use,  \nreproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ([https://us.sagepub.com/en-us/nam/open-access-at-sage](https://us.sagepub.com/en-us/nam/open-access-at-","cbCaipGjhUmS3a1P","https://ap.wps.com/l/cbCaipGjhUmS3a1P","pdf",3974329,18,"English","# Introduction\n## Background and clinical rationale\n# Methods\n## Data sources and signature selection\n## Risk score construction and subgroup analyses\n## Experimental validation and nomogram building\n# Results\n## Five-gene signature and risk stratification\n## Associations with OS, TMB, TME, stemness, and drug sensitivity\n## Prognostic performance of the model and nomogram\n# Conclusion\n## Biomarker potential for prognosis and treatment choice","[{\"question\":\"What is the main goal of this study on anoikis-related genes in breast cancer?\",\"answer\":\"To identify anoikis-related gene signatures that can predict prognosis and characterize immune microenvironment and drug sensitivity in breast cancer patients.\"},{\"question\":\"How were the gene signatures selected and the risk score model constructed?\",\"answer\":\"Five anoikis-related signatures were selected from ARGs using univariate Cox regression, LASSO regression, and multivariate Cox regression, followed by building an ARGs risk score model to stratify patients into high- and low-risk groups.\"},{\"question\":\"Which patient and tumor characteristics were compared between high- and low-risk groups?\",\"answer\":\"Overall survival (OS), tumor mutation burden (TMB), tumor microenvironment (TME), stemness, and drug sensitivity were analyzed for associations with the risk groups.\"}]","Identification of Novel Anoikis-Related Gene Signatures to Predict the Prognosis, Immune Microenvironment, and Drug Sensitivity of Breast Cancer Patients - Cancer Control Volume 31 - Article 1–18 | PDF",1790004538,45]