GC-MS metabolomics identifies novel biomarkers to distinguish tuberculosis pleural effusion from malignant pleural effusion

• Main Authors: Cai, L. (Author), Chen, D. (Author), Liu, Y. (Author), Mei, B. (Author)
• Format: Article
• Language: English
• Published: John Wiley and Sons Inc 2021
• Subjects: adult; aged; Aged; Article; biochemical analysis; biological marker; biomarker; Biomarkers; citric acid; clinical article; cluster analysis; Cluster Analysis; controlled study; creatinine; cystine; Diagnosis, Differential; diagnostic test accuracy study; diagnostic value; differential diagnosis; fatty acid; female; Female; Gas Chromatography-Mass Spectrometry; GC-MS; glycine; human; Humans; male; Male; malignant pleura effusion; malignant pleural effusion; mass fragmentography; Metabolic Networks and Pathways; metabolism; metabolite; metabolome; Metabolome; metabolomics; Metabolomics; middle aged; Middle Aged; multivariate analysis; Multivariate Analysis; oleamide; ornithine; pleura effusion; Pleural Effusion, Malignant; principal component analysis; Principal Component Analysis; pyroglutamic acid; quantitative analysis; receiver operating characteristic; reproducibility; Reproducibility of Results; ROC Curve; sensitivity and specificity; stearic acid; tuberculosis; Tuberculosis; tuberculosis pleural effusion; tuberculous pleural effusion; urea; valine
• Online Access: View Fulltext in Publisher

 Background: Tuberculous pleural effusions (TBPEs) and malignant pleural effusions (MPEs) are two of the most common and severe forms of exudative effusions. Clinical differentiation is challenging; however, metabolomics is a collection of powerful tools currently being used to screen for disease-specific biomarkers. Methods: 17 TBPE and 17 MPE patients were enrolled according to the inclusion criteria. The normalization gas chromatography-mass spectrometry (GC-MS) data were imported into the SIMCA-P + 14.1 software for multivariate analysis. The principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to analyze the data, and the top 50 metabolites of variable importance projection (VIP) were obtained. Metabolites were qualitatively analyzed using the National Institute of Standards and Technology (NIST) databases. Pathway analysis was performed by MetaboAnalyst 4.0. The detection of biochemical indexes such as urea and free fatty acids in these pleural effusions was also verified, and significant differences were found between these two groups. Results: 1319 metabolites were screened by non-targeted metabonomics of GC-MS. 9 small molecules (urea, L-5-oxoproline, L-valine, DL-ornithine, glycine, L-cystine, citric acid, stearic acid, and oleamide) were found to be significantly different (p < 0.05 for all). In OPLS-DA, 9 variables were considered significant for biological interpretation (VIP≥1). However, after the ROC curve was performed, it was found that the metabolites with better diagnostic value were stearic acid, L-cystine, citric acid, free fatty acid, and creatinine (AUC > 0.8), with good sensitivity and specificity. Conclusion: Stearic acid, L-cystine, and citric acid may be potential biomarkers, which can be used to distinguish between the TBPE and the MPE. © 2021 The Authors. Journal of Clinical Laboratory Analysis published by Wiley Periodicals LLC.