迫切需要更好的生物标志物来检测早期乳腺癌。利用非靶向代谢组学和脂质组学以及先进的数据挖掘方法来进行以代谢为中心的生物标志物的发现和验证，可以增强用于乳腺癌筛查的新型生物标志物的识别和验证。在这项研究中，我们采用多模式组学方法来识别和验证能够区分乳腺癌患者和良性肿瘤患者的潜在生物标志物。我们的研究结果表明，醚联磷脂酰胆碱在浸润性导管癌和良性肿瘤之间表现出显着差异，包括乳房X光检查结果不一致的病例。我们观察到乳腺癌组中多种脂质种类的变化，包括鞘磷脂、三酰甘油和游离脂肪酸。此外，我们还发现了乳腺癌中几种失调的亲水性代谢物，例如谷氨酸、甘氨鹅去氧胆酸盐和二甲基尿酸。通过利用机器学习模型（线性支持向量机或随机森林模型）进行稳健的多变量接收器操作特征分析，我们成功区分了癌症和良性病例，并取得了良好的结果。这些结果强调了代谢生物标志物补充乳腺癌筛查中其他标准的潜力。未来的研究对于进一步验证我们研究中确定的代谢生物标志物并开发临床应用的检测方法至关重要。版权所有：© 2024 Anh 等人。这是一篇根据知识共享署名许可条款分发的开放获取文章，允许在任何媒体上不受限制地使用、分发和复制，前提是注明原始作者和来源。

There is an urgent need for better biomarkers for the detection of early-stage breast cancer. Utilizing untargeted metabolomics and lipidomics in conjunction with advanced data mining approaches for metabolism-centric biomarker discovery and validation may enhance the identification and validation of novel biomarkers for breast cancer screening. In this study, we employed a multimodal omics approach to identify and validate potential biomarkers capable of differentiating between patients with breast cancer and those with benign tumors. Our findings indicated that ether-linked phosphatidylcholine exhibited a significant difference between invasive ductal carcinoma and benign tumors, including cases with inconsistent mammography results. We observed alterations in numerous lipid species, including sphingomyelin, triacylglycerol, and free fatty acids, in the breast cancer group. Furthermore, we identified several dysregulated hydrophilic metabolites in breast cancer, such as glutamate, glycochenodeoxycholate, and dimethyluric acid. Through robust multivariate receiver operating characteristic analysis utilizing machine learning models, either linear support vector machines or random forest models, we successfully distinguished between cancerous and benign cases with promising outcomes. These results emphasize the potential of metabolic biomarkers to complement other criteria in breast cancer screening. Future studies are essential to further validate the metabolic biomarkers identified in our study and to develop assays for clinical applications.Copyright: © 2024 Anh et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.