Xiaowei Song
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Xiaowei Song has a total of 3 co-authors. Here are the top co-authors by number of shared publications:
Songhai Xie
Department of General Surgery, The Sixth Medical Center of Chinese PLA General Hospital, Beijing, China
1 shared publicationYan Zhang
Department of General Surgery, The Sixth Medical Center of Chinese PLA General Hospital, Beijing, China
1 shared publicationYaoyao Song
Department of General Surgery, The Sixth Medical Center of Chinese PLA General Hospital, Beijing, China
1 shared publication
Latest Publications
- Open Access
- 15 December 2022
- Published by Frontiers Media SA in Frontiers in Cell and Developmental Biology
- Vol. 10, 1075810
Screening and diagnosis of triple negative breast cancer based on rapid metabolic fingerprinting by conductive polymer spray ionization mass spectrometry and machine learning
We present the use of conductive spray polymer ionization mass spectrometry (CPSI-MS) combined with machine learning (ML) to rapidly gain the metabolic fingerprint from 1 μl liquid extraction from the biopsied tissue of triple-negative breast cancer (TNBC) in China. The 76 discriminative metabolite markers are verified at the primary carcinoma site and can also be successfully tracked in the serum. The Lasso classifier featured with 15- and 22-metabolites detected by CPSI-MS achieve a sensitivity of 88.8% for rapid serum screening and a specificity of 91.1% for tissue diagnosis, respectively. Finally, the expression levels of their corresponding upstream enzymes and transporters have been initially confirmed. In general, CPSI-MS/ML serves as a cost-effective tool for the rapid screening, diagnosis, and precise characterization for the TNBC metabolism reprogramming in the clinical practice.
- 15 December 2022
- Published by Frontiers Media SA in Frontiers in Cell and Developmental Biology
- Vol. 10, 1075810
Open AccessScreening and diagnosis of triple negative breast cancer based on rapid metabolic fingerprinting by conductive polymer spray ionization mass spectrometry and machine learning
We present the use of conductive spray polymer ionization mass spectrometry (CPSI-MS) combined with machine learning (ML) to rapidly gain the metabolic fingerprint from 1 μl liquid extraction from the biopsied tissue of triple-negative breast cancer (TNBC) in China. The 76 discriminative metabolite markers are verified at the primary carcinoma site and can also be successfully tracked in the serum. The Lasso classifier featured with 15- and 22-metabolites detected by CPSI-MS achieve a sensitivity of 88.8% for rapid serum screening and a specificity of 91.1% for tissue diagnosis, respectively. Finally, the expression levels of their corresponding upstream enzymes and transporters have been initially confirmed. In general, CPSI-MS/ML serves as a cost-effective tool for the rapid screening, diagnosis, and precise characterization for the TNBC metabolism reprogramming in the clinical practice.