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Using TCGA and METABRIC datasets, CTRGs were derived by WGCNA, then a prognostic CTR score was built from CTRGs selected by seven machine-learning algorithms. The CTR score was linked to TME, immunotherapy, and drug sensitivity, with CTRG expression further explored via single-cell and spatial transcriptomics and validated by RT-PCR in clinical tissues. 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Prognostic Signature to Predict Clinical Outcomes and Treatment Response in Breast Cancer Patients  \nBaoai Wu 1,†, Longpeng Li 1,†, Longhui Li 2, Yinghua Chen 1, Yue Guan 1 and Jinfeng Zhao 1, *  \nCitation: Wu, B.; Li, L.; Li, L.; Chen, Y.; Guan, Y.; Zhao, J. Integration of Bioinformatics and Machine Learning to Identify CD8+ T Cell-Related Prognostic Signature to Predict Clinical Outcomes and Treatment Response in Breast Cancer Patients. Genes 2024, 15, 1093. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)[10.3390/genes15081093](10.3390/genes15081093)  \nAcademic Editor: Hongyan Xu  \nReceived: 19 July 2024  \nRevised: 15 August 2024  \nAccepted: 16 August 2024  \nPublished: 19 August 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Institute of Physical Education and Sport, Shanxi University, Taiyuan 030006, China; [wf670720@sxu.edu.cn](wf670720@sxu.edu.cn) (B.W.); [lp200721@163.com](lp200721@163.com) (L.L.); [c1846905590@163.com](c1846905590@163.com) (Y.C.); [18809802068@163.com](18809802068@163.com) (Y.G.)  \n2 Capital University of Physical Education and Sports, Beijing 100191, China; [m15235524119@163.com](m15235524119@163.com)  \n* [Correspondence: zjf@sxu.edu.cn](Correspondence: zjf@sxu.edu.cn)  \n† These authors contributed equally to this work.  \nAbstract: The incidence of breast cancer (BC) continues to rise steadily, posing a significant burden on the public health systems of various countries worldwide. As a member of the tumor microenvironment (TME), CD8+ T cells inhibit cancer progression through their protective role. This study aims to investigate the role of CD8+ T cell-related genes (CTRGs) in breast cancer patients. Methods: We assessed the abundance of CD8+ T cells in the TCGA and METABRIC datasets and obtained CTRGs through WGCNA. Subsequently, a prognostic signature (CTR score) was constructed from CTRGs screened by seven machine learning algorithms, and the relationship between the CTR score and TME, immunotherapy, and drug sensitivity was analyzed. Additionally, CTRGs’ expression indifferent cells within TME was identified through single-cell analysis and spatial transcriptomics. Finally, the expression of CTRGs in clinical tissues was verified via RT-PCR. Results: The CD8+ T cell-related prognostic signature consists of two CTRGs. In the TCGA and METABRIC datasets, the CTR score appeared to be negatively linked to the abundance of CD8+ T cells, and BC patients with higher risk score show a worse prognosis. The low CTR score group exhibits higher immune infiltration levels, closely associated with inhibiting the tumor microenvironment. Compared with the high CTR score group, the low CTR score group shows better responses to chemotherapy and immune checkpoint therapy. Single-cell analysis and spatial transcriptomics reveal the heterogeneity of two CTRGs in different cells. Compared with the adjacent tissues, CD163L1 and KLRB1 mRNA are downregulated in tumor tissues. Conclusions: This study establishes a robust CD8+ T cell-related prognostic signature, providing new insights for predicting the clinical outcomes and treatment responses of breast cancer patients.  \nKeywords: breast cancer; machine learning; CD8+ T cell; tumor microenvironment; prognostic signature  \n1. Introduction  \nBreast cancer is one of the main diseases that threaten women’s health and cause disability and death [1] . Global data from 2020 reveal 2.3 million new cases of breast cancer, representing approximately 12% of all newly diagnosed cancers, along with 685,000 related deaths [2] . With the success of novel systemic therap","cbCain5ZOFhTFCNG","https://ap.wps.com/l/cbCain5ZOFhTFCNG","pdf",28681434,"English","# Introduction\n## Background and biomarkers\n# Materials and Methods\n## Data sources and CTRG identification\n## Signature construction and algorithm screening\n# Results\n## Prognostic performance and clinical associations\n## Immune infiltration and therapy response\n## Single-cell and spatial transcriptomics findings\n## RT-PCR validation","[{\"question\":\"What is the study’s main goal for breast cancer patients?\",\"answer\":\"To build and evaluate a CD8+ T cell–related prognostic signature that predicts clinical outcomes and treatment response in breast cancer patients.\"},{\"question\":\"How were CD8+ T cell–related genes (CTRGs) and the CTR score obtained?\",\"answer\":\"CTRGs were derived from TCGA and METABRIC data using WGCNA, then a prognostic CTR score was constructed from CTRGs selected by seven machine-learning algorithms.\"},{\"question\":\"What associations were found between the CTR score and tumor microenvironment or treatment response?\",\"answer\":\"Higher-risk patients had worse prognosis, while the low-CTR score group showed higher immune infiltration and better responses to chemotherapy and immune checkpoint therapy.\"}]","Integration of Bioinformatics and Machine Learning to Identify CD8+ T Cell-Related Prognostic Signature to Predict Clinical Outcomes and Treatment Response in Breast Cancer Patients | PDF",1790003694]