Aim: We aimed to identify new plasma biomarkers for the diagnosis of Pulmonary tuberculosis.
Background: Tuberculosis is an ancient infectious disease that remains one of the major global health
problems. Until now, effective, convenient, and affordable methods for diagnosis of Pulmonary tuberculosis
were still lacking.
Objective: This study focused on constructing a label-free LC-MS/MS-based comparative proteomics
between six tuberculosis patients and six healthy controls to identify differentially expressed proteins
(DEPs) in plasma.
Methods: To reduce the influences of high-abundant proteins, albumin and globulin were removed from
plasma samples using affinity gels. Then DEPs from the plasma samples were identified using a label-free
Quadrupole-Orbitrap LC-MS/MS system. The results were analyzed by the protein database search algorithm
SEQUEST-HT to identify mass spectra to peptides. The predictive abilities of combinations of host
markers were investigated by general discriminant analysis (GDA), with leave-one-out cross-validation.
Results: A total of 572 proteins were identified and 549 proteins were quantified. The threshold for differentially
expressed protein was set as adjusted p-value< 0.05 and fold change ≥1.5 or ≤0.6667, 32
DEPs were found. ClusterVis, TBtools, and STRING were used to find new potential biomarkers of
PTB. Six proteins, LY6D, DSC3, CDSN, FABP5, SERPINB12, and SLURP1, which performed well in
the LOOCV method validation, were termed as potential biomarkers. The percentage of cross-validated
grouped cases correctly classified and original grouped cases correctly classified is greater than or equal
Conclusion: We successfully identified five candidate biomarkers for immunodiagnosis of PTB in
plasma, LY6D, DSC3, CDSN, SERPINB12, and SLURP1. Our work supported this group of proteins
as potential biomarkers for pulmonary tuberculosis, and be worthy of further validation.