乳腺癌
医学
肿瘤科
癌症
内科学
代谢组学
生物标志物
癌症研究
生物信息学
计算生物学
生物
生物化学
作者
Yida Huang,Shaoqian Du,Jun Liu,Weiyi Huang,Wanshan Liu,Mengji Zhang,Ning Li,Ruimin Wang,Jiao Wu,Wei Chen,Mengyi Jiang,Tianhao Zhou,Jing Cao,Jing Yang,Lin Huang,An Gu,Jingyang Niu,Yuan Cao,Wei‐Xing Zong,Xin Wang,Jun Liu,Kun Qian,Hongxia Wang
标识
DOI:10.1073/pnas.2122245119
摘要
Significance Breast cancer (BrCa) is the most common cancer worldwide, and high-performance metabolic analysis is emerging in diagnosis and prognosis of BrCa. Here, we used nanoparticle-enhanced laser desorption/ionization mass spectrometry to record serum metabolic fingerprints of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection. Our analytical method, combined with the aid of machine learning algorithms, was demonstrated to provide high diagnostic efficiency with accuracy of 88.8% and desirable prognostic prediction ( P < 0.005). Furthermore, seven metabolic biomarkers differentially enriched in BrCa serum and their related pathways were identified. Together, our findings provide a tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.
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