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

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 WangJun Liu, Kun Qian, Hongxia Wang

Research output: Contribution to journalArticlepeer-review

51 Scopus citations


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.

Original languageEnglish (US)
Article numbere2122245119
JournalProceedings of the National Academy of Sciences of the United States of America
Issue number12
StatePublished - Mar 22 2022

All Science Journal Classification (ASJC) codes

  • General


  • Breast cancer
  • Diagnosis
  • Mass spectrometry
  • Prognosis
  • Serum metabolic fingerprints


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