Analysis of CD4, CD8, CD19, CD56-16, CD64, QuantiFERON biomarkers in exudative lymphocyte-dominant pleural effusion

Abstract

A BSTRACT Background: There are two main causes of exudative effusion including malignancy-induced effusion and tuberculosis. Considering that in reactive ejections, such as tuberculosis-induced effusion, the role of B lymphocytes and in the malignant effusion, the role of T lymphocytes are more important, in this study we analyzed the frequency of CD4, CD8, CD19, CD56-16, CD64, QuantiFERON in the pleural and serum samples of patients with exudative lymphocytic-dominant effusion. Methods: In total, 73 patients were enrolled in the study by exudative lymphocyte effusion, and finally, 63 patients had definite diagnoses. The patients were sorted into three groups including malignant, tuberculosis, and none. The sample of blood plasma and pleural effusion were collected and CD markers were analyzed using flow cytometry. Results: The mean age in the malignancy and tuberculous (TB) groups was 63.16 ± 12 and 52.15 ± 22.62, respectively. There was no significant difference in the frequency of CD8, CD4, and CD16-56 cells in blood samples of patients with tuberculosis and malignancy. Compared to those with tuberculosis, the percentage of CD64 cells was significantly higher in patients with tuberculosis than in malignant subjects. Moreover, a comparison of the frequency of cells with CD8, CD4, CD19, CD64, CD16-56, and CD14 markers in pleural samples showed no significant difference between groups. Other inflammatory factors were also investigated. The erythrocyte sedimentation rate (ESR) value for tuberculosis patients was significantly higher than malignancy. Also, QuantiFERON was positive in 14.3% of malignant patients, and 62.5% of patients with TB, which had a significant difference. Conclusion: Considering that there are many confounding variables in the study, such as previous medications, subtypes of Mycobacterium , and race of patients conducting studies in different groups and performing data mining for using a set of parameters can be used to detect the exact diagnosis.