Adenosine Deaminase in Pleural Effusion and Its Relationship with Clinical Parameters in Patients with Malignant Pleural Mesothelioma

Diagnostic flowchart for tuberculous pleurisy, pleural infection, and malignant pleural effusion

BACKGROUND

Several markers for the diagnosis of pleural effusion have been reported; however, a comprehensive evaluation using those markers has not been performed. Therefore, this study aimed to develop a diagnostic flowchart for tuberculous pleurisy, pleural infection, malignant pleural effusion, and other diseases by using these markers.

METHODS

We retrospectively collected data from 174 patients with tuberculous pleurisy, 215 patients with pleural infection other than tuberculous pleurisy, 360 patients with malignant pleural effusion, and 209 patients with other diseases at Fukujuji Hospital from January 2012 to October 2022. The diagnostic flowchart for four diseases was developed by using several previously reported markers.

RESULTS

The flowchart was developed by including seven markers: pleural ADA ≥40 IU/L, pleural fluid LDH <825 IU/L, pleural fluid ADA/TP < 14, neutrophil predominance or cell degeneration, peripheral blood WBC ≥9200/μL or serum CRP ≥12 mg/dL, pleural amylase ≥75 U/L, and the presence of pneumothorax according to the algorithm of a decision tree. The accuracy ratio of the flowchart was 71.7 % for the diagnosis of the four diseases, with 79.3 % sensitivity and 75.4 % positive predictive value (PPV) for tuberculosis pleurisy, 75.8 % sensitivity and 83.2 % PPV for pleural infection, 88.6 % sensitivity and 68.8 % PPV for malignant pleural effusion, and 33.0 % sensitivity and 60.0 % PPV for other diseases in the flowchart. The misdiagnosis ratios were 4.6 % for tuberculosis pleurisy, 6.8 % for pleural infection, and 8.3 % for malignant pleural effusion.

CONCLUSION

This study developed a useful diagnostic flowchart for tuberculous pleurisy, pleural infection, malignant pleural effusion, and other diseases.

Chronic Inflammation, Oxidative Stress and Metabolic Plasticity: Three Players Driving the Pro-Tumorigenic Microenvironment in Malignant Mesothelioma

Malignant pleural mesothelioma (MPM) is a lethal and rare cancer, even if its incidence has continuously increased all over the world. Asbestos exposure leads to the development of mesothelioma through multiple mechanisms, including chronic inflammation, oxidative stress with reactive oxygen species (ROS) generation, and persistent aberrant signaling. Together, these processes, over the years, force normal mesothelial cells' transformation. Chronic inflammation supported by "frustrated" macrophages exposed to asbestos fibers is also boosted by the release of pro-inflammatory cytokines, chemokines, growth factors, damage-associated molecular proteins (DAMPs), and the generation of ROS. In addition, the hypoxic microenvironment influences MPM and immune cells' features, leading to a significant rewiring of metabolism and phenotypic plasticity, thereby supporting tumor aggressiveness and modulating infiltrating immune cell responses. This review provides an overview of the complex tumor-host interactions within the MPM tumor microenvironment at different levels, i.e., soluble factors, metabolic crosstalk, and oxidative stress, and explains how these players supporting tumor transformation and progression may become potential and novel therapeutic targets in MPM.

Hemorrhagic pleural effusion in Indonesian male with pulmonary tuberculosis: A rare case

Background

Patients with hemorrhagic pleural effusion who live in tuberculosis endemic areas are recommended to perform adenosine deaminase (ADA) test.

Case presentation

A Javanese 22-year-old male complained of shortness of breath and cough with phlegm for 1 week, and worsened 3 days before being admitted to the hospital. The X-ray results showed pleural effusion, and hemorrhagic pleural effusion examination showed an increase in lymphocytes (60.2%), lactate dehydrogenase/LDH (2624 U/L), and cell count (4584 cells/mm³), and the ADA test obtained 49 IU/L. The water-sealed drainage (WSD) was installed and first-line anti-tuberculosis drug (ATD) was given for 1 month. After showing improvement in the first month, the first-line ATD was continued until 6 months.

Discussion

Patients with hemorrhage pleural effusion who live in tuberculosis endemic areas are recommended to perform differential diagnosis of hemorrhage pleural effusion and pulmonary tuberculosis. The use of the first-line ATD in hemorrhagic pleural effusion and pulmonary tuberculosis needs to be evaluated in the first month to detect improvement, otherwise, the medication is stopped and other investigations are carried out.

Conclusion

Successful management of hemorrhagic pleural effusion and pulmonary tuberculosis depends on early diagnosis.

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The diagnosis of tuberculosis pleural effusion is considered in endemic tuberculosis.

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Management of hemorrhagic tuberculosis pleural effusion includes chest tube and anti-tuberculosis drug (ATD).

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Hemorrhagic tuberculosis pleural effusion can be confirmed by the ADA test.

Diagnostic value of adenosine deaminase in bronchoalveolar lavage fluid for patients with lung cancer

BACKGROUND

Lung cancer is both the most common seen malignity and cause of cancer-related deaths worldwide. Adenosine deaminase (ADA) is a hydrolytic enzyme that catalyses the conversion of adenosine to inosine in the purine metabolism pathway. Studies examining ADA levels in bronchoalveolar lavage (BAL) fluids of patients with lung malignancy are very limited in the literature. Our aim examine the clinical significance of ADA levels in BAL fluids of patients with lung malignancy.

METHODS

A total of 89 patients undergoing fiberoptic bronchoscopy (FOB) with different indications from December 2017 to December 2018 were included in this study. The patients were divided into two groups as malignancy and non-malignancy groups. Demographic, laboratory data and ADA levels in bronchoalveolar lavage (BAL) were compared between the two groups. In addition, ADA levels in BAL were compared among the histopathological subtypes of patients in the malignant group.

RESULTS

The mean age of the patients was 58.2 ± 14.5 years with 86% of male gender. ADA enzyme levels were statistically higher in the malignant patient group compared with the non-malignant group (37.2 [17.6-71] vs 17.1 [9-35.3], P < .001). When the patients in the malignant group were compared in terms of ADA levels according to their histopathological types, a statistically significant difference was obtained in small cell carcinoma patients (49 [12.5-75.3], P = .005).

CONCLUSION

ADA levels in BAL may be a diagnostic biomarker in lung malignancies. In patients where a biopsy cannot be taken or histopathological typing cannot be performed because of tissue insufficiency, ADA levels in BAL can be an auxiliary parameter in making malignancy / histopathological diagnosis accompanied by radiological and clinical findings.

Diagnostic accuracy of adenosine deaminase for pleural tuberculosis in a low prevalence setting: A machine learning approach within a 7-year prospective multi-center study

OBJECTIVE

To analyze the performance of adenosine deaminase in pleural fluid combined with other parameters routinely measured in clinical practice and assisted by machine learning algorithms for the diagnosis of pleural tuberculosis in a low prevalence setting, and secondly, to identify effusions that are non-tuberculous and most likely malignant.

PATIENTS AND METHODS

We prospectively analyzed 230 consecutive patients diagnosed with lymphocytic exudative pleural effusion from March 2013 to June 2020. Diagnosis according to the composite reference standard was achieved in all cases. Pre-test probability of pleural tuberculosis was 3.8% throughout the study period. Parameters included were: levels of adenosine deaminase, pH, glucose, proteins, and lactate dehydrogenase, red and white cell counts and lymphocyte percentage in pleural fluid, as well as age. We tested six different machine learning-based classifiers to categorize the patients. Two different classifications were performed: a) tuberculous/non-tuberculous and b) tuberculous/malignant/other.

RESULTS

Out of a total of 230 patients with pleural effusion included in the study, 124 were diagnosed with malignant effusion and 44 with pleural tuberculosis, while 62 were given other diagnoses. In the tuberculous/non-tuberculous classification, and taking into account the validation predictions, the support vector machine yielded the best result: an AUC of 0.98, accuracy of 97%, sensitivity of 91%, and specificity of 98%, whilst in the tuberculous/malignant/other classification, this type of classifier yielded an overall accuracy of 80%. With this three-class classifier, the same sensitivity and specificity was achieved in the tuberculous/other classification, but it also allowed the correct classification of 90% of malignant cases.

CONCLUSION

The level of adenosine deaminase in pleural fluid together with cell count, other routine biochemical parameters and age, combined with a machine-learning approach, is suitable for the diagnosis of pleural tuberculosis in a low prevalence scenario. Secondly, non-tuberculous effusions that are suspected to be malignant may also be identified with adequate accuracy.

Identification of DNA repair-related genes predicting pathogenesis and prognosis for liver cancer

Background

Liver cancer (LC) is one of the most fatal cancers throughout the world. More efficient and sensitive gene signatures that could accurately predict survival in LC patients are vitally needed to promote a better individualized and effective treatment.

Material/methods

422 LC and adjacent normal tissues with both RNA-Seq and clinical data in TCGA were embedded in our study. Gene set enrichment analysis (GSEA) was applied to identify genes and hallmark gene sets that are more valuable for liver cancer therapy. Cox regression analysis was used to identify genes related to overall survival (OS) and build the prediction model. cBioPortal database was used to examine the alterations of the panel mRNA signature. ROC curves and Kaplan–Meier curves were used to validate the prediction model. Besides, the expression of the genes in the model were validated using quantitative real-time PCR in clinical tissue specimens.

Results

The panel of DNA repair-related mRNA signature consisted of seven mRNAs: RFC4 (replication factor C subunit 4), ZWINT (ZW10 interacting kinetochore protein), UPF3B (UPF3B regulator of nonsense mediated mRNA decay), NCBP2 (nuclear cap binding protein subunit 2), ADA (adenosine deaminase), SF3A3 (splicing factor 3a subunit 3) and GTF2H1 (general transcription factor IIH subunit 1). On-line analysis of cBioPortal database found that the expression of the panel mRNA has a wide variation ranging from 7 to 10%. All the mRNAs were significantly upregulated in LC tissues compared to normal tissues (P < 0.05). The risk model is closely related to the OS of LC patients. The hazard ratio (HR) is 2.184 [95% CI (confidence interval) 1.523–3.132] and log-rank P-value < 0.0001. For clinical specimen validation, we found that all of the genes in the model upregulated in liver cancer tissues versus normal liver tissues, which was consistent with the results predicted.

Conclusions

Our study demonstrated a mRNA signature including seven mRNA for prognosis prediction of LC. This panel gene signature provides a new criterion for accurate diagnosis and therapeutic target of LC.

Nucleotide Metabolism Behind Epigenetics

The mechanisms of epigenetic gene regulation-histone modifications, chromatin remodeling, DNA methylation, and noncoding RNA-use metabolites as enzymatic cofactors and substrates in reactions that allow chromatin formation, nucleotide biogenesis, transcription, RNA processing, and translation. Gene expression responds to demands from cellular processes that use specific metabolites and alters or maintains cellular metabolic status. However, the roles of metabolites-particularly nucleotides-as regulatory molecules in epigenetic regulation and biological processes remain largely unknown. Here we review the crosstalk between gene expression, nucleotide metabolism, and cellular processes, and explore the role of metabolism in epigenetics as a critical regulator of biological events.