数据表2：基于机器学习模型的乳腺癌端粒酶特征预后评估
基于多组学数据和机器学习构建的乳腺癌端粒酶预后特征模型（MLTS），用于准确预测患者预后。
基本信息
资源简介
数据表2：基于机器学习模型的乳腺癌端粒酶特征预后评估
【一句话概览】
本数据集是 figshare 公开的多模态数据集，收录 1 个文件、合计 648.35 KB，主题涵盖 breast cancer、telomerase genes、machine learning、PD-1、gemcitabine，可用于跨模态对齐与融合建模研究，CC BY 4.0 许可开放获取。
【数据集背景】
本数据集源站标题为《Data Sheet 2_Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.pdf》。配套论文《Integrative analysis from multi-center studies identities a consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer》发表于 eBioMedicine（2022），DOI：10.1016/j.ebiom.2021.103750。源站首次发布：2024-11-28。
【数据内容】
源站原始描述：BackgroundBreast 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. MethodsWe 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 associate…
中文译文（机器翻译，仅供快速理解，请以原文为准）：背景 由于乳腺癌具有分子异质性和复杂性，其预后仍然是一项重大挑战。准确的预测模型对于改善患者结局和定制个性化治疗至关重要。 方法 我们通过整合来自九个独立乳腺癌数据集的多组学数据，开发了一种机器学习辅助端粒酶特征（MLTS）。使用多种机器学习算法，我们识别出六个与患者生存显著相关的端粒酶相关基因。我们在多样化队列中，将MLTS的预测性能与66个现有乳腺癌预后模型进行了评估比较。 结果 与其他模型相比，MLTS表现出更优越的预测准确性、稳定性和可靠性。MLTS评分高的患者表现出肿瘤突变负荷增加、染色体不
【数据结构与技术规格】
文件数 1 个，合计 648.35 KB。Data Sheet 2_Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.pdf：application/pdf，648.35 KB。数据集 DOI：10.3389/fimmu.2024.1462953.s002。
【主题与分类】
源站学科分类：Genetic Immunology。主题标签：breast cancer、telomerase genes、machine learning、PD-1、gemcitabine。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Evaluating_the_prognostic_potential_of_telomerase_signature_in_breast_cancer_through_advanced_machine_learning_model_pdf/27923901
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Guo, Xiao; Cao, Yuyan; Shi, Xinlin; Xing, Jiaying; Feng, Chuanbo; Wang, Tao (2024). Data Sheet 2_Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.pdf. Frontiers. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Data Sheet 2_Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.pdf
- DOI：10.3389/fimmu.2024.1462953.s002
- 发布日期：2024-11-28
- 文件数：1 个
- 文件总体积：648.35 KB
- 源站页面：https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Evaluating_the_prognostic_potential_of_telomerase_signature_in_breast_cancer_through_advanced_machine_learning_model_pdf/27923901
命令行下载
curl -L -o "Data Sheet 2_Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.pdf" "https://ndownloader.figshare.com/files/50857587"
文件清单
Data Sheet 2_Evaluating the prognostic potential of telomerase signature in breast cancer through advanced machine learning model.pdf（648.35 KB）
文件总体积 648.35 KB（源站 API 实测）。
数据说明
BackgroundBreast 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.
MethodsWe 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.
ResultsThe 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.
ConclusionsOur 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 as a target for personalized therapy. Future research may focus on integrating MLTS with additional molecular signatures to enhance its clinical application in precision oncology.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.3389/fimmu.2024.1462953.s002（source: https://frontiersin.figshare.com/articles/dataset/Data_Sheet_2_Evaluating_the_prognostic_potential_of_telomerase_signature_in_breast_cancer_through_advanced_machine_learning_model_pdf/27923901）。
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