Development and validation of an inflammatory biomarkers model to predict gastric cancer prognosis: a multi-center cohort study in China.

BACKGROUND: Inflammatory factors have increasingly become a more cost-effective prognostic indicator for gastric cancer (GC). The goal of this study was to develop a prognostic score system for gastric cancer patients based on inflammatory indicators. METHODS: Patients' baseline characteristics and anthropometric measures were used as predictors, and independently screened by multiple machine learning(ML) algorithms. We constructed risk scores to predict overall survival in the training cohort and tested risk scores in the validation. The predictors selected by the model were used in multivariate Cox regression analysis and developed a nomogram to predict the individual survival of GC patients. RESULTS: A 13-variable adaptive boost machine (ADA) model mainly comprising tumor stage and inflammation indices was selected in a wide variety of machine learning models. The ADA model performed well in predicting survival in the validation set (AUC = 0.751; 95% CI: 0.698, 0.803). Patients in the study were split into two sets - "high-risk" and "low-risk" based on 0.42, the cut-off value of the risk score. We plotted the survival curves using Kaplan-Meier analysis. CONCLUSION: The proposed model performed well in predicting the prognosis of GC patients and could help clinicians apply management strategies for better prognostic outcomes for patients.

miR-223-3p inhibits the progression of atherosclerosis via down-regulating the activation of MEK1/ERK1/2 in macrophages.

BACKGROUND: microRNAs (miRNAs) have drawn more attention to the progression of atherosclerosis (AS), due to their noticeable inflammation function in cardiovascular disease. Macrophages play a crucial role in disrupting atherosclerotic plaque, thereby we explored the involvement of miR-223-3p in the inflammatory response in macrophages. METHODS: RT-qPCR was used to analyze the miR-223-3p levels in carotid arteries and serum of AS patients. ROC curve was used to assess the diagnostic value of miR-223-3p. Movat staining was applied to evaluate the morphological differences. FISH was used to identify the expression of miR-223-3p in macrophages of atherosclerotic lesions. Bioinformatic analysis was performed. Double-immunofluorescence and western blot were performed to assess the inflammatory cytokine secretion and p-ERK1/2. C16-PAF was injected into the culture medium of the miR-223-3p mimic/NC-transfected macrophages with ox-LDL. RESULTS: MiR-223-3p was up-regulated in AS patients and was associated with a higher overall survival rate. MiR-223-3p was co-localized with CD68+ macrophages in vulnerable atherosclerotic lesions. MiR-223-3p mimics decreased atherosclerotic lesions, macrophages numbers whereas increased SMCs numbers in the lesions. The TNF-a immune-positive areas were reduced by miR-223-3p mimics. MAP2K1 was negatively associated with miR-223-3p. MiR-223-3p mimics reduced the inflammation and the MEK1/ERK1/2 signaling pathway in vivo and in vitro. C16-PAF reversed the effects of miR-223-3p mimics on inflammation and ERK1/2 signaling pathway. CONCLUSIONS: MiR-223-3p negatively regulates inflammatory responses by the MEK1/ERK1/2 signaling pathway. Our study provides new insight into how miR-223-3p protects against atherosclerosis, representing a broader therapeutic prospect for treating atherosclerosis by miR-223-3p.