Lung cancer is the leading cause of cancer-related deaths among men and women in the world, accounting for the 25% of cancer mortality. Early diagnosis is an unmet clinical issue. In this work, we focused to develop a novel approach to identify highly sensitive and specific biomarkers by investigating the use of extracellular vesicles (EVs) isolated from the pleural lavage, a proximal fluid in lung cancer patients, as a source of potential biomarkers. We isolated EVs by ultracentrifuge method from 25 control pleural fluids and 21 pleural lavages from lung cancer patients. Analysis of the expression of EV-associated miRNAs was performed using Taqman OpenArray technology through which we could detect 288 out of the 754 miRNAs that were contained in the OpenArray. The differential expression analysis yielded a list of 14 miRNAs that were significantly dysregulated (adj. p-value < 0.05 and logFC lower or higher than 3). Using Machine Learning approach we discovered the lung cancer diagnostic biomarkers; miRNA-1-3p, miRNA-144-5p and miRNA-150-5p were found to be the best by accuracy. Accordance with our finding, these miRNAs have been related to cancer processes in previous studies. This results opens the avenue to the use of EV-associated miRNA of pleural fluids and lavages as an untapped source of biomarkers, and specifically, identifies miRNA-1-3p, miRNA-144-5p and miRNA 150-5p as promising biomarkers of lung cancer diagnosis.

The aim of this study was to know the treatment effect of video-assisted thoracic surgery (VATS) on 90-day mortality after anatomical lung resection based on a nationwide cohort. This is a multicentre prospective cohort of 2721 anatomical resections for lung cancer from December 2016 to March 2018. Treatment and intention-to-treat (ITT) analyses were performed after inverse probability score weighting and different propensity score matching algorithms. Covariate balance was assessed by standardized mean differences. The estimators reported were the average treatment effect, the average treatment effect on the treated and odds ratios after conditional logistic models with 95% confidence intervals. The unconfoundedness assumption was evaluated by sensitivity analysis for average treatment effect (c-dependence) and average treatment effect on the treated (Γ). VATS was the initial approach in 1911 patients (70.2%), though 273 cases (14.3%) had to be converted to thoracotomy. Ninety-day mortality rates were: treatment analysis (VATS 1.16% vs open 3.9%, P < 0.001), ITT analysis (VATS 1.78% vs open 3.36%, P = 0.012). After inverse probability score weighting and propensity score matching, in the treatment analysis, VATS meant absolute risk reductions between 2.25% and 2.96% and relative risk reductions between 65% and 70% [OR = 0.34 (95% confidence interval 0.15–0.79), all P-values <0.004). However, all the estimators turned out to be non-significant in the ITT analyses. A high sensitivity to unobservable confounders was proved (c-dependence 0.135, Γ = 1.5). VATS can reduce the risk of 90-day mortality after anatomical lung resection. However, the implications of conversion to thoracotomy, comparing ITT versus treatment analysis, and the potential impact of hidden bias should deserve further attention in the future.