Dataset Information

Assessment of CD27 expression on T-cells as a diagnostic and therapeutic tool for patients with smear-negative pulmonary tuberculosis.

ABSTRACT:

Background

There is a global focus on illness diagnosis in smear-negative and latent tuberculosis infectious populations (SN-TB and LTBI). CD27 has been suggested to play a direct role in active TB. Little is known about smear-negative individuals. Here, we tried to investigate whether it has a role in smear-negative populations. The expression of CD27 and MTB-specific CD27 in CD4⁺ T cells ("CD27^-CD4⁺" and "CD27^-IFN-γ⁺CD4⁺") was evaluated in MTB-unexposed controls (HC), TB contacts (TB-C) and SN-TB individuals by flow cytometry. The sensitivity, specificity and AUC (area under curve) of "CD27^-IFN-γ⁺CD4⁺" cells to distinguish SN-TBs from HCs and TB-Cs were determined by receiver operating characteristic (ROC) curve analysis. The clinical index was selected from the clinical laboratory and evaluated for correlation with "CD27^-IFN-γ⁺CD4⁺" cells by Spearman statistical analysis.

Results

We observed that the percentages of "CD27^-IFN-γ⁺CD4⁺" cells were significantly increased in the SN-TB group compared with the HC and TB-C groups (AUC was 0.88, sensitivity was 82.14%, specificity was 80.00%, and P < 0.0001). The percentage of "CD27^-IFN-γ⁺CD4⁺" cells was negatively correlated with WBC (white blood cell count) (r = - 0.3019, P = 0.0182) and positively correlated with IgE (immunoglobulin E) (r = 0.2805, P = 0.0362). Furthermore, "CD27^-IFN-γ⁺CD4⁺" cells were significantly decreased, especially in the > 50 years group, after clinical treatment.

Conclusion

The present results demonstrated that the percentage of "CD27^-IFN-γ⁺CD4⁺" cells might be a conceivable molecular indicator in the diagnosis of SN-TB and was influenced by its outcome of therapy.

SUBMITTER: Xu F

PROVIDER: S-EPMC8237462 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundTuberculosis (TB) had been the leading lethal infectious disease worldwide for a long time (2014-2019) until the COVID-19 global pandemic, and it is still one of the top 10 death causes worldwide. One important reason why there are so many TB patients and death cases in the world is because of the difficulties in precise diagnosis of TB using common detection methods, especially for some smear-negative pulmonary tuberculosis (SNPT) cases. The rapid development of metabolome and machine learning offers a great opportunity for precision diagnosis of TB. However, the metabolite biomarkers for the precision diagnosis of smear-positive and smear-negative pulmonary tuberculosis (SPPT/SNPT) remain to be uncovered. In this study, we combined metabolomics and clinical indicators with machine learning to screen out newly diagnostic biomarkers for the precise identification of SPPT and SNPT patients.MethodsUntargeted plasma metabolomic profiling was performed for 27 SPPT patients, 37 SNPT patients and controls. The orthogonal partial least squares-discriminant analysis (OPLS-DA) was then conducted to screen differential metabolites among the three groups. Metabolite enriched pathways, random forest (RF), support vector machines (SVM) and multilayer perceptron neural network (MLP) were performed using Metaboanalyst 5.0, "caret" R package, "e1071" R package and "Tensorflow" Python package, respectively.ResultsMetabolomic analysis revealed significant enrichment of fatty acid and amino acid metabolites in the plasma of SPPT and SNPT patients, where SPPT samples showed a more serious dysfunction in fatty acid and amino acid metabolisms. Further RF analysis revealed four optimized diagnostic biomarker combinations including ten features (two lipid/lipid-like molecules and seven organic acids/derivatives, and one clinical indicator) for the identification of SPPT, SNPT patients and controls with high accuracy (83-93%), which were further verified by SVM and MLP. Among them, MLP displayed the best classification performance on simultaneously precise identification of the three groups (94.74%), suggesting the advantage of MLP over RF/SVM to some extent.ConclusionsOur findings reveal plasma metabolomic characteristics of SPPT and SNPT patients, provide some novel promising diagnostic markers for precision diagnosis of various types of TB, and show the potential of machine learning in screening out biomarkers from big data.