A Multi-Omics-Based Prognostic Model for Elderly Breast Cancer by Machine Learning: Insights From Hypoxia and Immunity of Tumor Microenvironment.

IF 2.9 3区医学 Q2 ONCOLOGY

Clinical breast cancer Pub Date : 2025-04-15 DOI:10.1016/j.clbc.2025.04.008

Yu Song, Changjun Wang, Yidong Zhou, Qiang Sun, Yan Lin

{"title":"A Multi-Omics-Based Prognostic Model for Elderly Breast Cancer by Machine Learning: Insights From Hypoxia and Immunity of Tumor Microenvironment.","authors":"Yu Song, Changjun Wang, Yidong Zhou, Qiang Sun, Yan Lin","doi":"10.1016/j.clbc.2025.04.008","DOIUrl":null,"url":null,"abstract":"Introduction: Older adult breast cancer (OABC) patients (≥ 65 years) frequently experience poorer prognoses compared to younger adults, attributed to complex tumor biology and age-related factors. The present study employs a multiomics approach combined with machine learning to develop a novel prognostic model for OABC, with a focus on the hypoxic and immune characteristics of the tumor microenvironment.Methods: Genetic and molecular data from 503 OABC and 589 younger adult breast cancer (YABC) patients were analyzed using The Cancer Genome Atlas (TCGA) database. An ensemble machine-learning model was developed, integrating multiomics data-including mRNA, miRNA, lncRNA, copy number variations (CNVs), and single nucleotide variants (SNVs)-along with clinicopathological features, to predict survival outcomes. The model was trained on 300 OABC samples and validated on 203 samples.Results: The ensemble machine-learning model achieved a predictive accuracy of 69.5% for survival outcomes in OABC patients. Distinct hypoxia-related gene expression patterns and reduced immune cell infiltration were observed in OABC compared to YABC. Hypoxia was significantly associated with poorer disease-free survival (DFS) in OABC (P = .037), but not in YABC (P = .38).Conclusions: The multiomics-based prognostic model developed for OABC showed clinical potential, and the findings highlight the critical role of hypoxia and the immune microenvironment in the prognosis of OABC. Further research is warranted to validate this model in larger cohorts and to explore its potential application in guiding personalized treatment strategies for OABC patients.","PeriodicalId":10197,"journal":{"name":"Clinical breast cancer","volume":" ","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical breast cancer","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1016/j.clbc.2025.04.008","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Introduction: Older adult breast cancer (OABC) patients (≥ 65 years) frequently experience poorer prognoses compared to younger adults, attributed to complex tumor biology and age-related factors. The present study employs a multiomics approach combined with machine learning to develop a novel prognostic model for OABC, with a focus on the hypoxic and immune characteristics of the tumor microenvironment.

Methods: Genetic and molecular data from 503 OABC and 589 younger adult breast cancer (YABC) patients were analyzed using The Cancer Genome Atlas (TCGA) database. An ensemble machine-learning model was developed, integrating multiomics data-including mRNA, miRNA, lncRNA, copy number variations (CNVs), and single nucleotide variants (SNVs)-along with clinicopathological features, to predict survival outcomes. The model was trained on 300 OABC samples and validated on 203 samples.

Results: The ensemble machine-learning model achieved a predictive accuracy of 69.5% for survival outcomes in OABC patients. Distinct hypoxia-related gene expression patterns and reduced immune cell infiltration were observed in OABC compared to YABC. Hypoxia was significantly associated with poorer disease-free survival (DFS) in OABC (P = .037), but not in YABC (P = .38).

Conclusions: The multiomics-based prognostic model developed for OABC showed clinical potential, and the findings highlight the critical role of hypoxia and the immune microenvironment in the prognosis of OABC. Further research is warranted to validate this model in larger cohorts and to explore its potential application in guiding personalized treatment strategies for OABC patients.

查看原文本刊更多论文

基于机器学习的老年乳腺癌多组学预后模型：来自缺氧和肿瘤微环境免疫的见解

老年乳腺癌（OABC）患者（≥65岁）的预后往往比年轻人差，这是由于复杂的肿瘤生物学和年龄相关因素。本研究采用多组学方法结合机器学习，开发了一种新的OABC预后模型，重点关注肿瘤微环境的缺氧和免疫特征。方法：采用癌症基因组图谱（TCGA）数据库对503例OABC和589例YABC患者的遗传和分子数据进行分析。开发了一个集成机器学习模型，整合多组学数据，包括mRNA， miRNA, lncRNA，拷贝数变异（cnv）和单核苷酸变异（snv），以及临床病理特征，以预测生存结果。该模型在300个OABC样本上进行了训练，在203个样本上进行了验证。结果：集成机器学习模型对OABC患者生存结局的预测准确率达到69.5%。与YABC相比，OABC中观察到不同的缺氧相关基因表达模式和免疫细胞浸润减少。缺氧与OABC患者较差的无病生存期（DFS）显著相关（P = .037），而与YABC患者无显著相关（P = .38）。结论：基于多组学的OABC预后模型具有临床潜力，研究结果强调了缺氧和免疫微环境在OABC预后中的关键作用。进一步的研究需要在更大的队列中验证该模型，并探索其在指导OABC患者个性化治疗策略方面的潜在应用。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Clinical breast cancer 医学-肿瘤学

CiteScore

5.40

自引率

3.20%

发文量

174

审稿时长

48 days

期刊介绍： Clinical Breast Cancer is a peer-reviewed bimonthly journal that publishes original articles describing various aspects of clinical and translational research of breast cancer. Clinical Breast Cancer is devoted to articles on detection, diagnosis, prevention, and treatment of breast cancer. The main emphasis is on recent scientific developments in all areas related to breast cancer. Specific areas of interest include clinical research reports from various therapeutic modalities, cancer genetics, drug sensitivity and resistance, novel imaging, tumor genomics, biomarkers, and chemoprevention strategies.