Identification of a Four-Biomarker Panel for the Diagnosis of Tuberculous Pleural Effusion Using Olink Proteomics
Journal of Inflammation Research, 2026
Xing X., Guo C., Zhao G., Xie D., Zhang L., Shi K., Zhang Z., Pang Y., Cui J.
| Disease area | Application area | Sample type | Products |
|---|---|---|---|
Respiratory Diseases Infetious Diseases | Patient Stratification | Pleural Effusion Fluid |  Olink Target 96 |
Abstract
Background
Tuberculous pleural effusion (TPE) results from an inflammatory response triggered by tuberculosis infection. If not promptly diagnosed and treated, it can lead to severe pulmonary dysfunction and the risk of infection spread. However, TPE diagnosis remains a significant challenge. This study employs Olink proteomics to explore novel methods for diagnosing TPE.
Methods
In this study, we collected 20 cases each of TPE, malignant pleural effusion (MPE), and parapneumonic pleural effusion (PPE) from patients at the First Affiliated Hospital of Xinxiang Medical University in Xinxiang, China, between January and April 2024. Using Olink proteomics, we quantified 92 inflammation-related proteins in pleural effusions across these three patient groups. Differentially expressed proteins were identified, followed by enrichment and pathway analyses to explore potential underlying mechanisms. An independent validation cohort, consisting of 36 TPE samples and 29 non-tuberculous pleural effusion samples collected between April and July 2024, was used to validate the diagnostic performance of selected biomarkers by enzyme-linked immunosorbent assay (ELISA). Diagnostic accuracy was evaluated using logistic regression and receiver operating characteristic (ROC) curve analysis.
Results
A total of 92 inflammation-related proteins were identified in this study. Differential analysis identified 43 proteins with distinct expression levels between TPE and MPE, and 33 between TPE and PPE. Of these, 25 proteins were uniquely expressed in TPE. ELISA validation confirmed the expression of four key inflammatory proteins: IFN-γ, CXCL9, TNF-β and PD-L1. The combined area under the curve (AUC) for these markers was 0.963, with a sensitivity of 0.944 and specificity of 1, surpassing the sensitivity and specificity of individual or other biomarker combinations.
Conclusion
This study identified a diagnostic method using a combination of four biomarkers: IFN-γ, CXCL9, TNF-β, and PD-L1, which can aid in the diagnosis of TPE.