使用机器学习的乳腺癌缺氧和乳酸代谢预后评分(HLMPS)的开发和验证。

IF 1.7 4区 医学 Q4 ONCOLOGY
Translational cancer research Pub Date : 2025-07-30 Epub Date: 2025-07-27 DOI:10.21037/tcr-2025-1115
Zhou Fang, Shichong Liao, Zhong Wang, Juanjuan Li, Lijun Wang, Yimin Zhang, Yueyue Guo, Feng Yao
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引用次数: 0

摘要

背景:以往的研究往往忽视了缺氧和乳酸代谢在乳腺癌(BRCA)微环境中的作用。本研究利用机器学习方法开发并验证了一种基于缺氧相关基因(HRGs)和乳酸代谢相关基因(LMRGs)的新型BRCA预后模型。目的是鉴定能够预测患者预后和治疗反应的分子亚型,从而促进BRCA的精准医学策略。方法:本研究利用来自癌症基因组图谱(TCGA) BRCA队列的大量rna测序数据(1,079个肿瘤样本;以99个正常样本为训练集,从GEO数据库中检索5个独立验证队列(GSE19615、GSE20685、GSE20711、GSE42568、GSE58812)。从分子特征数据库(MSigDB)中鉴定出hrg和lmrg。采用基于机器学习的综合方法,通过10倍交叉验证和多种算法组合构建缺氧和乳酸代谢预后评分(HLMPS)。通过Kaplan-Meier生存分析、随时间变化的受试者工作特征(ROC)曲线和Brier评分量化的校准图,严格评估模型的稳健性。结果:HLMPS模型显示出强大的预后辨别能力,与低风险患者相比,高风险患者的总生存率明显低于低风险患者[训练集曲线下面积(aus): 0.76, 0.77, 0.74, 1/3/5年;验证集auc: 0.61, 0.65, 0.67(1/3/5年)。功能富集分析显示,高HLMPS患者往往存在细胞周期和神经发育途径的失调,而低HLMPS患者表现出免疫途径的激活,包括t细胞受体(TCR)信号传导和抗原递呈。免疫浸润分析显示,低HLMPS患者免疫细胞浸润水平较高,对免疫治疗的反应性较好。同时,低HLMPS患者对伊立替康、帕博西尼等药物更敏感,而高HLMPS患者对拉帕替尼、索拉非尼等药物更敏感。结论:HLMPS模型是预测BRCA患者预后和治疗反应的一种新颖且临床可行的工具。该研究强调了基于肿瘤微环境(TME)特征整合hrg和LMRGs的精准医疗策略的潜力。未来的工作应侧重于在更大的多中心队列中验证HLMPS模型,并确定其在指导BRCA患者个性化治疗决策方面的临床适用性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Development and validation of a Hypoxia and Lactate Metabolism Prognostic Score (HLMPS) for breast cancer using machine learning.

Background: Previous studies often overlooked the roles of hypoxia and lactate metabolism in the breast cancer (BRCA) microenvironment. This study developed and validated a novel prognostic model for BRCA based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs) using machine learning approaches. The aim was to identify molecular subtypes capable of predicting patient prognosis and treatment response, thereby facilitating precision medicine strategies for BRCA.

Methods: This study utilized bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA) BRCA cohort (1,079 tumor samples; 99 normal samples) as the training set, with five independent validation cohorts (GSE19615, GSE20685, GSE20711, GSE42568, GSE58812) retrieved from the Gene Expression Omnibus (GEO) database. HRGs and LMRGs were identified from the Molecular Signatures Database (MSigDB). A machine learning-based integrative approach was employed to construct the Hypoxia and Lactate Metabolism Prognostic Score (HLMPS) via 10-fold cross-validation and multiple algorithm combinations. Model robustness was rigorously assessed through Kaplan-Meier survival analysis, time-dependent receiver operating characteristic (ROC) curves, and calibration plots with Brier score quantification.

Results: The HLMPS model demonstrated robust prognostic discrimination, with high-risk patients exhibiting significantly inferior overall survival compared to low-risk counterparts [training set areas under the curve (AUCs): 0.76, 0.77, 0.74 at 1/3/5 years; validation sets AUCs: 0.61, 0.65, 0.67 at 1/3/5 years]. Functional enrichment analysis revealed that patients with a high HLMPS tended to have dysregulation of cell cycle and neurodevelopmental pathways, while those with a low HLMPS exhibited activation of immune pathways, including T-cell receptor (TCR) signaling and antigen presentation. An Immune infiltration analysis showed that patients with a low HLMPS had higher levels of immune cell infiltration and better responsiveness to immunotherapy. Meanwhile, patients with a low HLMPS showed greater sensitivity to drugs such as irinotecan and palbociclib, while patients with a high HLMPS were more sensitive to drugs such as lapatinib and sorafenib.

Conclusions: The HLMPS model represents a novel and clinically actionable tool for prognosticating outcomes and therapeutic responses in BRCA patients. This study highlights the potential of precision medicine strategies that integrate HRGs and LMRGs based on tumor microenvironment (TME) features. Future work should focus on validating the HLMPS model in larger, multicenter cohorts and determining its clinical applicability in guiding personalized treatment decisions for patients with BRCA.

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来源期刊
CiteScore
2.10
自引率
0.00%
发文量
252
期刊介绍: Translational Cancer Research (Transl Cancer Res TCR; Print ISSN: 2218-676X; Online ISSN 2219-6803; http://tcr.amegroups.com/) is an Open Access, peer-reviewed journal, indexed in Science Citation Index Expanded (SCIE). TCR publishes laboratory studies of novel therapeutic interventions as well as clinical trials which evaluate new treatment paradigms for cancer; results of novel research investigations which bridge the laboratory and clinical settings including risk assessment, cellular and molecular characterization, prevention, detection, diagnosis and treatment of human cancers with the overall goal of improving the clinical care of cancer patients. The focus of TCR is original, peer-reviewed, science-based research that successfully advances clinical medicine toward the goal of improving patients'' quality of life. The editors and an international advisory group of scientists and clinician-scientists as well as other experts will hold TCR articles to the high-quality standards. We accept Original Articles as well as Review Articles, Editorials and Brief Articles.
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