ArticleJCI insight2024
Granzyme A as biomarker for diagnosis in tuberculous pleural effusion.
Article in JCI insight, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Biochemistry-based machine learning algorithms in differentiating pleural effusion: current status and perspective.ERJ open research · 2026Review
- Development and validation of a diagnostic model for malignant pleural effusion based on random forest.Journal of thoracic disease · 2026Article
- Derivation and validation of a clinical prediction model incorporating the pleural fluid ADA-to-LDH ratio for differentiating tuberculous from malignant pleural effusions: a multi-center study.BMC pulmonary medicine · 2026Article
- Multi-omics nominates VDAC2 as a candidate protective locus in sepsis-associated cholesterol dysregulation.Apoptosis : an international journal on programmed cell death · 2025Article
- High diagnostic accuracy of flexible thoracoscopy for distinguishing tuberculous from malignant pleural effusions.American journal of translational research · 2025Article
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Authors and funding
13 authors.
Funding
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Abstract
BACKGROUNDCurrent diagnostic tools for tuberculous pleural effusion (TPE) are often inadequate, making accurate diagnosis challenging. Effective identification of TPE is critical for ensuring proper treatment and preventing tuberculosis relapse. This study explored the potential of granzyme A (GZMA) as a biomarker for TPE.METHODSPatients with TPE, malignant pleural effusion (MPE), and parapneumonic pleural effusion (PPE) were recruited into discovery and validation cohorts. The discovery cohort consisted of 200 patients with TPE and 100 patients with MPE, while the validation cohort included 167 patients with TPE, 84 patients with MPE, and 69 patients with PPE.RESULTSIn the discovery cohort, GZMA levels were significantly elevated in TPE compared with MPE, demonstrating 90% sensitivity and 91% specificity at a cutoff of 102.29 ng/mL for effectively distinguishing between the two conditions. In the validation cohort, GZMA maintained high diagnostic performance, distinguishing TPE from MPE with 87% sensitivity and 87% specificity and from PPE with 87% sensitivity and 84% specificity. Incorporating GZMA, lactate dehydrogenase (LDH), and adenosine deaminase (ADA) into a random forest model further improved diagnostic accuracy. In the discovery cohort, this model achieved 92% sensitivity and 100% specificity, and in the validation cohort, it distinguished TPE from MPE with 87% sensitivity and 94% specificity and from PPE with 87% sensitivity and 91% specificity.CONCLUSIONOverall, GZMA is a promising biomarker for diagnosing TPE, with improved accuracy when combined with LDH and ADA, providing a robust tool for timely identification and effective management of patients with TPE.FUNDINGThe study was supported by Science and Technology Project of Shenzhen (KCXFZ20211020163545004, KQTD20210811090219022, JCYJ20220818095610021, JSGG20220822095200001, JCYJ20210324094614038), Shenzhen Medical Research Funding (B2302035, A2302004), Provincial Natural Science Foundation of Guangdong (2022A1515220034), and Shenzhen Third People's Hospital Research Foundation (G2022155).
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