加强早期乳腺癌检测需要将额外的筛查方法与当前的诊断成像相结合。使用易于收集的血清样本进行组学筛选可以作为第一步。除了生物标志物识别功能之外，组学分析还可以对常见的组织学类型（DCIS 和 IDC）进行全面分析。采用代谢组学、金属组学和机器学习，可以产生准确的筛选模型，并对生物体反应提供有价值的见解。使用两种不同的分析方法，即用于代谢组学的质子 NMR 和用于金属组学的 ICP-OES，利用确诊乳腺癌患者的血清样本来分析代谢物和金属离子谱。然后对所得反应进行判别分析、进展生物标志物探索、患者代谢物和金属离子之间的相关性检查以及年龄和绝经状态的影响。测量的 NMR 谱和代谢物相对积分用于通过乳腺癌组和对照组之间的 MVA 实现统计学上显着的区分。分析确定了对区分至关重要的 24 种代谢物和 4 种金属离子。此外，四种代谢物与疾病进展相关。此外，代谢物相对积分、金属离子浓度和年龄/绝经状态亚组之间存在重要的相关性和关系。量化的相对积分可以区分研究的亚组，并通过保留集进行验证。代谢组学和金属组学的特征重要性和统计分析提取了一组常见实体，这些实体结合起来为持续的分子扰动和疾病进展提供了有价值的见解。© 2024。作者。

Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types-DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients' metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.© 2024. The Author(s).