Differential Diagnosis Of Malignant Pleural Effusion Research Articles

Exosomal miRNA-profiling of pleural effusion in lung adenocarcinoma and tuberculosis.

Pleural effusion (PE) caused by lung cancer is prevalent, and it is difficult to differentiate it from PE caused by tuberculosis. Exosome-based liquid biopsy offers a non-invasive technique to diagnose benign and malignant PE. Exosomal miRNAs are potential diagnostic markers and play an essential role in signal transduction and biological processes in tumor development. We hypothesized that exosomal miRNA expression profiles in PE would contribute to identifying its diagnostic markers and elucidating the molecular basis of PE formation in lung cancer. The exosomes from PE caused by lung adenocarcinoma (LUAD) and pulmonary tuberculosis were isolated and verified by transmission electron microscopy. The exosomal miRNA profiles were identified using deep sequencing and validated with quantitative real-time PCR (qRT-PCR). We performed bioinformatic analysis for differentially expressed miRNAs to explore how exosomal miRNAs regulate pleural effusion. We identified 99 upregulated and 91 downregulated miRNAs in malignant pleural effusion (MPE) compared to tuberculous pleural effusion (TPE). Seven differentially expressed miRNAs (DEmiRNAs) were validated by qRT-PCR, out of which 5 (71.4%) were confirmed through sequencing. Gene Ontology (GO) analysis revealed that most exosomal miRNAs target genes were involved in regulating cellular processes and nitrogen compound metabolism. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, the exosomal miRNAs target genes were mainly involved in Fc gamma R-mediated phagocytosis, Rap1 signaling pathway, and breast cancer. The hub genes, including ITGAM, FOXO1, MAPK14, YWHAB, GRIN1, and PRF1, were screened through plug-in cytoHubba. The PFR1 was identified as a critical gene in MPE formation using single-cell sequencing analysis. Additionally, we hypothesized that tumor cells affected natural killer cells and promoted the generation of PE in LUAD via the exosomal hsa-miR-3120-5p-PRF1 axis. We identified exosomal miRNA profiles in LUAD-MPE and TPE, which may help in the differential diagnosis of MPE and TPE. Bioinformatic analysis revealed that these miRNAs might affect PE generation through tumor immune response in LUAD. Our results provided a new theoretical basis for understanding the function of exosomal miRNAs in LUAD-MPE.

Use of Platelet Parameters in the Differential Diagnosis of Lung Adenocarcinoma-Associated Malignant Pleural Effusion and Tuberculous Pleural Effusion.

Background Both malignant pleural effusion (MPE) and tuberculous pleural effusion (TPE) are common etiologies of pleural effusion; the present study was conducted to establish the diagnostic value of platelet parameters in the differential diagnosis of MPE and TPE. Methods This retrospective study enrolled patients with lung adenocarcinoma-associated MPE and TPE. Platelet parameter data, including platelet count (PLT), mean platelet volume (MPV), plateletcrit (PCT), platelet distribution width (PDW), and platelet-larger cell ratio (P-LCR), were collected. Principal component analysis and multiple logistic regression modelling were carried out to assess the diagnostic value of these platelet parameters. Results The MPE group and the TPE group enrolled 270 and 433 patients, respectively. Demographic characteristics of patients were more female and higher age in the MPE group. MPV, PDW, and P-LCR were significantly higher in MPE patients, while PLT and PCT were significantly higher in TPE patients. Principal component analysis generated two principal components (PCs) based on above platelet parameters. After adjusting for confounding factors including gender and age, multiple logistic regression showed positive association between PC1 and MPE. Conclusion Platelet parameters were potential biomarkers in distinguishing lung adenocarcinoma-associated MPE from TPE. A patient with lower PLT and PCT and higher MPV, PDW, and P-LCR was more likely to be diagnosed as the former. Principal component analysis and multiple logistic regression performed well in improving multicollinearity, adjusting confounding factors, and identifying important risk factors for MPE.

Easy-to-Use Scoring System Based on CT Pleural Fluid and Blood Features for Discriminating Malignant Pleural Effusions from Tuberculous Pleural Effusions: A Retrospective and Prospective Validation Study

Background: Distinguishing malignant pleural effusion (MPE) from tuberculous pleural effusion (TPE) is a challenge when symptom is atypical. Computed tomography (CT) scans and biochemical indicators are the most commonly used methods to evaluate pulmonary and pleural diseases, and the role of CT features (especially pleural and pleural fluid features) in the diagnosis of MPE or TPE is poorly described. Therefore, this study aims to establish and verify diagnostic models based on the characteristics of pleural and pleural effusion for the differential diagnosis of MPE and TPE. Methods: In this study, a first batch of 986 patients from Wuhan union hospital and Wuhan Jinyintan Hospital in the training set was used to develop differential diagnosis models. The CT features (including maximum pleural thickness at the non-mass/nodule, density of the pleural effusion, pleural mass/nodule, mediastinal pleural involvement, pleural wrap, pleural calcifications) and biochemical markers (including serum carcinoembryonic antigen (CEA), and adenosine deaminase (ADA) in the pleural fluid) were collected and subjected to logistic regression analyses. Then, two models were constructed: a model based on CT features (Model 1) and a model based on pleural fluid markers with or without blood biochemical markers (Models 2a/2b). The differential diagnostic model was displayed in the form of nomograms, which were validated by an independent prospective cohort (N=422). Findings: In the training set, eight variables were selected as significant risk factors for distinguishing MPE from TPE after multivariable regression analyses, i.e. age, sex, maximum pleural thickness at the non-nodule/mass, pleural fluid density, pleural nodule/mass, serum CEA, effusion CEA and effusion ADA. In Model 1, the area under the Receiver Operating Characteristic Curve (AUROC) was 0.970, yielding a sensitivity and specificity of 91.07% and 89.86% respectively in the training set, and AUROC = 0.968, sensitivity and specificity was 93.85% and 87.73% respectively in the validation set. In Model 2a, the AUROC, sensitivity and specificity for distinguishing MPE from TPE were 0.976, 92.73% and 93.66% in the training set, respectively, and 0.959, 93.50% and 87.84% in the validation set, respectively. In Model 2b, the AUROC, sensitivity and specificity for distinguishing MPE from TPE were 0.970, 94.87% and 90.87% in the training set, respectively, and 0.962, 96.77% and 80.25% in the validation set, respectively. There were no significant differences between the three models. Interpretation: CT features of pleura and pleural fluid could effectively help physicians differentiate between MPE and TPE, which were readily acquired and could be used as a non-invasive and convenient diagnostic tool in clinical practice. Trial Registration: This study was registered on the Clinical Trials website (No. NCT03997669). Funding Statement: This research was supported by the National Natural Science Foundation of China (No. 81770096; No. 81800094), the National Science and Technology Key Project (No. 2019ZX09301001). Declaration of Interests: The authors declare no competing non-financial/financial interests. Ethics Approval Statement: This study was approved by the Institutional Review Board of the Tongji Medical College, Huazhong University of Science and Technology, and supported by the Wuhan Clinical Research Center for Pleural Diseases