已发表论文

外科气雾剂中的脂质作为诊断肺癌的诊断生物标志物

 

Authors Zhang J, Zheng Q, Zhang W, Wang N, Xu J, Cheng X, Wei Y

Received 12 October 2018

Accepted for publication 23 April 2019

Published 17 June 2019 Volume 2019:11 Pages 5537—5543

DOI https://doi.org/10.2147/CMAR.S190634

Checked for plagiarism Yes

Review by Single-blind

Peer reviewers approved by Ms Justinn Cochran

Peer reviewer comments 4

Editor who approved publication: Dr Rituraj Purohit

Introduction: Lung cancer is one of the most common malignancies worldwide, and the main effective treatment is surgical operation to cure this disease. This study assessed the feasibility of surgical aerosol for identification of lung cancer and adjacent normal tissue in surgery.
Methods: In vitro experiments, the surgical aerosol was released when the tissue sample was being cut using a standard electrosurgery handpiece. Surgical smoke was dissolved in methanol by negative-pressure suction and then get to the neutral sprayer for analysis. Multivariate analysis was performed using partial least squares (PLS) analysis in MatLab 2011.
Results: A total of 208 surgical aerosol database entries were obtained from 26 patients. In the cancerous aerosol, relative abundance (760.61, 782.39, and 789.68 m/z) was increased, while relative abundances of (756.41 m/z) was decreased compared with normal-tissue aerosol. After PLS analysis, mass–spectrometry (MS) data for the cancer aerosol showed clear differentiation from normal. Four significant peaks were identified by collision-induced dissociation experiments. The cancerous aerosol showed overexpression of phosphatidylserine (34:2), phosphatidylcholine (36:4), and triacylglycerol (46:2), while phosphatidylcholine (34:3) was decreased. Coupling PLS and extractiveelectrospray-ionization MS analysis of the surgical aerosol data of lung cancer were clearly distinguished from normal.
Conclusion: The surgical aerosol might contain biomarkers for identification of lung cancer and normal tissue.
Keywords: lung cancer, biomarker, surgical aerosol, mass spectrometry




Figure 2 Discrimination of surgical aerosol mass-spectra data analyzed by PLS, demonstrating class separation between...