Granzyme A as biomarker for diagnosis in tuberculous pleural effusion
Fuxiang Li, Chuanzhi Zhu, Yue Zhang, Fanhui Kong, Ximeng Zhang, Liping Pan, Hongyan Jia, Liang Fu, Yunlong Hu, Guofang Deng, Qianting Yang, Xinchun Chen, Yi Cai
Fuxiang Li, Chuanzhi Zhu, Yue Zhang, Fanhui Kong, Ximeng Zhang, Liping Pan, Hongyan Jia, Liang Fu, Yunlong Hu, Guofang Deng, Qianting Yang, Xinchun Chen, Yi Cai
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Clinical Research and Public Health Infectious disease

Granzyme A as biomarker for diagnosis in tuberculous pleural effusion

Abstract

BACKGROUND Current 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.METHODS Patients 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.RESULTS In 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.CONCLUSION Overall, 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.FUNDING The 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).

Authors

Fuxiang Li, Chuanzhi Zhu, Yue Zhang, Fanhui Kong, Ximeng Zhang, Liping Pan, Hongyan Jia, Liang Fu, Yunlong Hu, Guofang Deng, Qianting Yang, Xinchun Chen, Yi Cai

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Figure 1

Study design and participant distribution across discovery and validation cohorts.

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Study design and participant distribution across discovery and validatio...
The figure presents a 2-stage study design with discovery and validation cohorts. The discovery phase included 200 patients with TPE and 100 patients with MPE. Among the TPE cases, 43 were from Shenzhen (SZ), 56 were from Harbin (HB), and 101 were from Beijing (BJ). For the MPE cases, 7 were from SZ, 20 were from HB, and 73 were from BJ. Activities included data collection; evaluation of biomarkers, such as ADA, LDH, and GZMA; and development and assessment of predictive models. The random forest model, identified as the most effective, established optimal cutoff values based on ROC curve analysis, accuracy, sensitivity, and specificity. These cutoffs were then tested in the validation cohort, which comprised 167 patients with TPE, 84 patients with MPE, and 69 patients with benign PPE, to evaluate the generalizability and effectiveness of the diagnostic thresholds. Among the validation cohort, TPE included 7 cases from SZ, 20 from HB, and 73 from BJ. MPE included 4 cases from SZ, 21 from HB, and 59 from BJ. PPE included 37 cases from SZ and 32 from HB. Metrics assessed included accuracy, sensitivity, specificity, and confusion matrix analysis.