Diagnosis and prognosis of breast cancer by high-performance serum metabolic fingerprints

High-performance metabolic analysis is emerging in the diagnosis and prognosis of breast cancer (BrCa). Still, advanced tools are in demand to deliver the application potentials of metabolic analysis. Here, we used fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS)...

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Published in:Proceedings of the National Academy of Sciences - PNAS 2022-03, Vol.119 (12), p.e2122245119
Main Authors: Huang, Yida, Du, Shaoqian, Liu, Jun, Huang, Weiyi, Liu, Wanshan, Zhang, Mengji, Li, Ning, Wang, Ruimin, Wu, Jiao, Chen, Wei, Jiang, Mengyi, Zhou, Tianhao, Cao, Jing, Yang, Jing, Huang, Lin, Gu, An, Niu, Jingyang, Cao, Yuan, Zong, Wei-Xing, Wang, Xin, Qian, Kun, Wang, Hongxia
Format: Article
Language:English
Subjects:
Biological Sciences
Biomarkers
Biomarkers, Tumor - metabolism
Breast cancer
Breast Neoplasms - diagnosis
Breast Neoplasms - metabolism
Diagnosis
Female
Fingerprints
Humans
Ionization
Lasers
Machine learning
Mass spectrometry
Mass Spectrometry - methods
Mass spectroscopy
Medical prognosis
Metabolism
Metabolites
Nanoparticles
Physical Sciences
Prognosis
Reproducibility of Results
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Summary:High-performance metabolic analysis is emerging in the diagnosis and prognosis of breast cancer (BrCa). Still, advanced tools are in demand to deliver the application potentials of metabolic analysis. Here, we used fast nanoparticle-enhanced laser desorption/ionization mass spectrometry (NPELDI-MS) to record serum metabolic fingerprints (SMFs) of BrCa in seconds, achieving high reproducibility and low consumption of direct serum detection without treatment. Subsequently, machine learning of SMFs generated by NPELDI-MS functioned as an efficient readout to distinguish BrCa from non-BrCa with an area under the curve of 0.948. Furthermore, a metabolic prognosis scoring system was constructed using SMFs with effective prediction performance toward BrCa (P < 0.005). Finally, we identified a biomarker panel of seven metabolites that were differentially enriched in BrCa serum and their related pathways. Together, our findings provide an efficient serum metabolic tool to characterize BrCa and highlight certain metabolic signatures as potential diagnostic and prognostic factors of diseases including but not limited to BrCa.
ISSN:0027-8424
1091-6490
1091-6490
DOI:10.1073/pnas.2122245119