[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123145-en":3,"doc-seo-123145-105":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},123145,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model","Breast cancer prognosis remains difficult due to molecular heterogeneity and complex disease biology, limiting the reliability of existing risk models for individualized treatment planning. This research develops a machine learning–assisted telomerase signature (MLTS) by integrating multi-omics information from nine independent breast cancer datasets and training multiple algorithms to select telomerase-related genes linked to survival outcomes. MLTS is benchmarked against 66 published prognostic models and validated through genomic, single-cell transcriptomic, and immune microenvironment analyses to support more stable prediction and potential clinical translation.","TYPE Original Research PUBLISHED 28 November 2024 DOI 10.3389/fimmu.2024.1462953  \nOPEN ACCESS  \nEDITED BY  \nWei Wang,  \nFirst Affiliated Hospital of Anhui Medical University, China  \nREVIEWED BY  \nRishi Kumar Jaiswal,  \nLoyola University Chicago, United States Heba Taher,  \nCairo University, Egypt  \n*CORRESPONDENCE  \nTao Wang  \n[wangtaoGPPH@gzu.edu.cn](wangtaoGPPH@gzu.edu.cn)  \n†These authors have contributed equally to this work  \nRECEIVED 10 July 2024  \nACCEPTED 14 November 2024  \nPUBLISHED 28 November 2024  \nCITATION  \nGuo X, Cao Y, Shi X, Xing J, Feng C and Wang T (2024) Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.  \nFront. Immunol. 15:1462953 .  \ndoi: 10.3389/fimmu.2024.1462953  \nCOPYRIGHT  \n© 2024 Guo, Cao, Shi, Xing, Feng 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.  \nEvaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model  \nXiao Guo 1†, Yuyan Cao 1†, Xinlin Shi 1, Jiaying Xing 1, Chuanbo Feng 1 and Tao Wang 2*  \n1School of Pharmacy, Beihua University, Jilin, Jilin, China, 2 Research Laboratory Center, Guizhou Provincial People’s Hospital, Guiyang, Guizhou, China  \nBackground: Breast cancer prognosis remains a significant challenge due to the disease's molecular heterogeneity and complexity. Accurate predictive models are critical for improving patient outcomes and tailoring personalized therapies.  \nMethods: We developed a Machine Learning-assisted Telomerase Signature (MLTS) by integrating multi-omics data from nine independent breast cancer datasets. Using multiple machine learning algorithms, we identified six telomerase-related genes significantly associated with patient survival. The predictive performance of MLTS was evaluated against 66 existing breast cancer prognostic models across diverse cohorts.  \nResults: The MLTS demonstrated superior predictive accuracy, stability, and reliability compared to other models. Patients with high MLTS scores exhibited increased tumor mutational burden, chromosomal instability, and poor survival outcomes. Single-cell RNA sequencing analysis further revealed higher MLTS scores in aneuploid tumor cells, suggesting a role in cancer progression. Immune profiling indicated distinct tumor microenvironment characteristics associated with MLTS scores, potentially guiding therapeutic decisions.  \nConclusions: Our findings highlight the utility of MLTS as a robust prognostic biomarker for breast cancer. The ability of MLTS to predict patient outcomes and its association with key genomic and cellular features underscore its potential asa target for personalized therapy. Future research may focus on integrating MLTS with additional molecular signatures to enhance its clinical application in precision oncology.  \nKEYWORDS  \nbreast cancer, telomerase genes, machine learning, PD-1, gemcitabine  \nFrontiers in Immunology 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nBreast cancer, historically labeled as the “invisible killer” in middle-aged and older women, has alarmingly started to proliferate among younger demographics at an unprecedented rate. Recent findings from the International Agency for Research on Cancer reveal a striking 24.2% incidence rate of breast cancer, underscoring a critical, escalating public health issue that continues to rise annually (1) .  \nWhile various prognostic models have been developed, such as those by Sui et al. using immune cell infiltration scores (2), and by [Elke M. et](Elke M. et) al. integrating genetic polymorphis","cbCaipqRao10bItX","https://ap.wps.com/l/cbCaipqRao10bItX","pdf",20188123,1,17,"English","en",105,"# Introduction\n## Clinical challenge and rationale\n## Telomerase and prior prognostic modeling\n# Methods\n## Data acquisition from public cohorts\n## Machine learning–assisted telomerase signature construction\n## Performance evaluation and comparison\n# Results\n## Predictive accuracy and stability versus existing models\n## Genomic features linked to MLTS scores\n## Single-cell and immune microenvironment findings\n# Conclusions\n## MLTS as a prognostic biomarker and future directions","[{\"question\":\"What is the main goal of the study on telomerase signatures in breast cancer?\",\"answer\":\"To build and evaluate a machine learning–assisted telomerase signature (MLTS) that predicts breast cancer patient survival more accurately than existing prognostic models.\"},{\"question\":\"How is the MLTS model constructed in this research?\",\"answer\":\"The study integrates multi-omics data from nine independent breast cancer datasets and uses multiple machine learning algorithms to identify six telomerase-related genes associated with survival.\"},{\"question\":\"What biological features are associated with high MLTS scores?\",\"answer\":\"High MLTS scores relate to increased tumor mutational burden and chromosomal instability, worse survival outcomes, higher MLTS scores in aneuploid tumor cells, and distinct immune microenvironment characteristics.\"}]","Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine 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is the main goal of the study on telomerase signatures in breast cancer?","Question",{"text":75,"@type":76},"To build and evaluate a machine learning–assisted telomerase signature (MLTS) that predicts breast cancer patient survival more accurately than existing prognostic models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the MLTS model constructed in this research?",{"text":80,"@type":76},"The study integrates multi-omics data from nine independent breast cancer datasets and uses multiple machine learning algorithms to identify six telomerase-related genes associated with survival.",{"name":82,"@type":73,"acceptedAnswer":83},"What biological features are associated with high MLTS scores?",{"text":84,"@type":76},"High MLTS scores relate to increased tumor mutational burden and chromosomal instability, worse survival outcomes, higher MLTS scores in aneuploid tumor cells, and distinct immune microenvironment 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