Serum levels of miR-19b and miR-146a as prognostic biomarkers for non-small cell lung cancer.

MicroRNA (miRNA) is a type of small non-coding RNA molecule that has important roles in cancer initiation, promotion and progression by negatively regulating gene expression. In this study, we explored the role of miRNAs in the prognosis of patients with non-small cell lung cancer (NSCLC). The miRNA expression profiles were determined in 5 pairs of NSCLC and paracancerous tissues (3 adenocarcinomas and 2 squamous cell carcinomas). Aberrantly expressed miRNAs were validated by quantitative real-time PCR (qRT-PCR) in 61 pairs of NSCLC and paracancerous tissues. Differentially expressed miRNAs were further analyzed in sera from 94 healthy subjects and 94 advanced NSCLC patients receiving platinum-based chemotherapy. Three miRNAs (miR-19b, miR-146a, and miR-223) were significantly dysregulated in NSCLC tissues (P < 0.05). High miR-19b and low miR-146a expression in NSCLC tissues were associated with higher TNM stage, lymph node metastasis and poorer survival (P < 0.05). The serum levels of miR-19b in NSCLC patients were significantly higher (P < 0.001), whereas serum levels of miR-146a were significantly lower (P < 0.001), compared with those in controls. Serum levels of miR-19b and miR-146a were associated with overall survival of NSCLC patients (P < 0.05). Patients with low serum level of miR-19b and high serum level of miR-146a achieved a higher overall response rate and longer survival time (P < 0.05). These data suggest that miR-19b and miR-146a are potential biomarkers for the prediction of survival and response to chemotherapy in NSCLC.

Development of nomograms predictive of anastomotic leakage in patients before minimally invasive McKeown esophagectomy.

Purpose: The present study aims to identify factors related to anastomotic leakage before esophagectomy and to construct a prediction model. Methods: A retrospective analysis of 285 patients who underwent minimally invasive esophagectomy (MIE). An absolute shrinkage and selection operator was applied to screen the variables, and predictive models were developed using binary logistic regression. Results: A total of 28 variables were collected in this study. LASSO regression analysis, combined with previous literature and clinical experience, finally screened out four variables, including aortic calcification, heart disease, BMI, and FEV1. A binary logistic regression was conducted on the four predictors, and a prediction model was established. The prediction model showed good discrimination and calibration, with a C-statistic of 0.67 (95% CI, 0.593-0.743), a calibration curve fitting a 45° slope, and a Brier score of 0.179. The DCA demonstrated that the prediction nomogram was clinically useful. In the internal validation, the C-statistic still reaches 0.66, and the calibration curve has a good effect. Conclusions: When patients have aortic calcification, heart disease, obesity, and a low FEV1, the risk of anastomotic leakage is higher, and relevant surgical techniques can be used to prevent it. Therefore, the clinical prediction model is a practical tool to guide surgeons in the primary prevention of anastomotic leakage.