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The comprehensibility of cellular senescence-related characteristics in TNBC remains unclear. Single-cell RNA sequencing with Seurat, ssGSEA, consensus clustering, immune estimation (ESTIMATE, MCP-counter, TIMER, EPIC), and TIDE were used to build subtype-specific risk models. Hub genes were validated by qRT-PCR and transwell assays, with ACP5 functionally promoting migration and invasion.",{"@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/research-and-experimental-verification-on-the-mechanisms-of-cellular-senescence-in-triple-negative-breast-cancer/331900/",{"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/research-and-experimental-verification-on-the-mechanisms-of-cellular-senescence-in-triple-negative-breast-cancer/331900.png","ImageObject",300,407,{"name":92,"@type":93},"Anda","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-31","2026-09-21",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"How were cellular senescence-related subtypes of TNBC identified in this study?","Question",{"text":112,"@type":113},"Single-cell RNA sequencing data were analyzed with Seurat, and senescence pathway scores were computed by ssGSEA. Consensus clustering based on these pathway features produced three TNBC clusters.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"What immune-related findings distinguished the TNBC clusters?",{"text":117,"@type":113},"Clusters 1 and 3 showed a high degree of immune infiltration. TIDE scores were higher in cluster 3, suggesting greater immune escape and a lower likelihood of benefiting from immunotherapy.",{"name":119,"@type":110,"acceptedAnswer":120},"Which genes were highlighted by the risk model and how were they validated?",{"text":121,"@type":113},"Univariate Cox and LASSO regression identified prognostic feature genes to construct and validate a TNBC risk model. RT-qPCR showed MMP28 and ACP5 and KRT6A up-regulated, while CT83 was down-regulated, supporting the bioinformatics results.","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},331900,1790398008,{"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":34,"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":145,"preview_page_count":4},962075006959,"https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297","Submitted 17 November 2023 Accepted 22 January 2024  \nPublished 29 February 2024 Corresponding author  \nTian Tang, [tt9772888@126.com](tt9772888@126.com)[ ](tt9772888@126.com)Academic editor  \nFanglin Guan  \nAdditional Information and Declarations can be found on page 23  \nDOI 10.7717/peerj.16935  \n Copyright 2024 Cao et al.  \nDistributed under  \nCreative Commons CC-BY 4.0  \nOPEN ACCESS  \nResearch and experimental verification on the mechanisms of cellular senescencein triple-negative breast cancer  \nTengfei Cao1 ,* , Mengjie Huang1 ,* , Xinyue Huang1 and Tian Tang2  \n1 Department of Breast Surgery, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China  \n2 Department of Pathology, The Second Affiliated Hospital of Guangzhou Medical University, Guangzhou, China  \n* These authors contributed equally to this work.  \nABSTRACT  \nBackground. Triple-negative breast cancer (TNBC) is an aggressive breast cancer subtype with high heterogeneity, poor prognosis, and a low 10-year survival rate of less than 50% . Although cellular senescence displays extensive effects on cancer, the comprehensions of cellular senescence-related characteristicsinTNBC patients remains obscure.  \nMethod. Single-cell RNA sequencing (scRNA-seq) data were analyzed by Seurat package. Scores for cellular senescence-related pathways were computed by singlesample gene set enrichment analysis (ssGSEA) . Subsequently, unsupervised consensus clustering was performed for molecular cluster identification. Immune scores of patients in The Cancer Genome Atlas (TCGA) dataset and associated immune cell scores were calculated using Estimation of STromal and Immune cells in MAlignantTumours using Expression data (ESTIMATE) and Microenvironment Cell Populations-counter (MCP-counter), Tumor Immune Estimation Resource (TIMER) and Estimating the Proportion of Immune and Cancer cells (EPIC) methods, respectively. Immunotherapy scores were assessed using TIDE. Furthermore, feature genes were identified by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses; these were used to construct a risk model. Additionally, quantitative reverse transcription-polymerase chain reaction (qRT-PCR) and transwell assay were conducted for in vitro validation of hub genes.  \nResult. TNBC was classified into three subtypes based on cellular senescence-related pathways as clusters 1, 2, and 3 . Specifically, cluster 1 showed the best prognosis, followed by cluster 2 and cluster 3 . The levels of gene expression in cluster 2 were the lowest, whereas these were the highest in cluster 3 . Moreover, clusters 1 and 3 showed a high degree of immune infiltration. TIDE scores were higher for cluster 3, suggesting that immune escape was more likely in patients with the cluster 3 subtype who were less likely to benefit from immunotherapy. Next, the TNBC risk model was constructed and validated. RT-qPCR revealed that prognostic risk genes (MMP28, ACP5 and KRT6A) were up-regulated while protective genes (CT83) were down-regulated in TNBC cell lines, validating the results of the bioinformatics analysis. Meanwhile, cellular experiments revealed that ACP5 could promote the migration and invasion abilities in two TNBC cell lines. Finally, we evaluated the validity of prognostic models for assessing TME characteristics and TNBC chemotherapy response.  \nHow to cite this article Cao T, Huang M, Huang X, Tang T. 2024. Research and experimental verification on the mechanisms of cellular senescence in triple-negative breast cancer. PeerJ 12:e16935 [http://doi.org/10.7717/peerj.16935](http://doi.org/10.7717/peerj.16935)  \nConclusion. In conclusion, these findings help to assess the efficacy of targeted therapies in patients with different molecular subtypes, have practical applications for subtypespecific treatment ofTNBC patients, and provide information on prognostic factors, as well as guidance for the revelation of the molecular mechanisms by which sen","cbCaimq5Bqgylgbq","https://ap.wps.com/l/cbCaimq5Bqgylgbq","pdf",7792290,32,"English","# Abstract\n## Background\n## Method\n## Result\n## Conclusion\n# Introduction","[{\"question\":\"How were cellular senescence-related subtypes of TNBC identified in this study?\",\"answer\":\"Single-cell RNA sequencing data were analyzed with Seurat, and senescence pathway scores were computed by ssGSEA. Consensus clustering based on these pathway features produced three TNBC clusters.\"},{\"question\":\"What immune-related findings distinguished the TNBC clusters?\",\"answer\":\"Clusters 1 and 3 showed a high degree of immune infiltration. TIDE scores were higher in cluster 3, suggesting greater immune escape and a lower likelihood of benefiting from immunotherapy.\"},{\"question\":\"Which genes were highlighted by the risk model and how were they validated?\",\"answer\":\"Univariate Cox and LASSO regression identified prognostic feature genes to construct and validate a TNBC risk model. RT-qPCR showed MMP28 and ACP5 and KRT6A up-regulated, while CT83 was down-regulated, supporting the bioinformatics results.\"}]","Research and experimental verification on the mechanisms of cellular senescence in triple-negative breast cancer | PDF",1789995661,81]