In the progression of various diseases, inflammation has a critical role. Chronic persistent inflammation is a pivotal trigger of fibrosis. Several microRNAs (miRNAs) participate in inflammation and fibrosis. In recent years, it has been proved that miRNAs are a critical link in physiological and pathological processes. Among them, the miRNA miR-146a has a pivotal role in the immune system and acquired immunity, making it one of the most studied miRNAs. Due to its essential roles at the molecular and cellular levels, it has broad application prospects in precision medicine. The present comprehensive review focused on the mechanisms of miR-146a and its application strategies in inflammation and fibrosis, discussing its therapeutic potential. The main signaling pathways through which miR-146a regulates inflammation and fibrosis and their relationships were covered. Furthermore, the functions and effects of miR-146a in specific cells, which may join in the process of inflammation and fibrosis, were outlined. Application strategies were also summarized according to recent studies based on these mechanisms. Contents 1. Introduction 2. Location and transcription regulation of miR-146a 3. miR-146a and inflammation 4. miR-146a and fibrosis 5. Application strategies 6. Conclusions and perspectives

Background The risk of lung cancer in nonsmokers is increasing; however, there are relatively few studies on the risks of lung cancer in nonsmokers. Patients and Methods We collected epidemiological and clinical data from 429 nonsmoking patients with lung nodules from the Affiliated Li Huili Hospital as a training cohort and 123 nonsmoking patients with lung nodules as a testing cohort. We identified variables that might be related to malignant lung nodules from 27 variables by performing least absolute shrinkage and selection operator analysis. Univariate and multivariate analyses of these variables were conducted using binary logistic regression. Significant variables were used to generate a lung cancer risk prediction model for nodules in nonsmokers. Results We successfully constructed a predictive nomogram incorporating density, ground‐glass opacities, pulmonary nodule size, hypertension, plasma fibrinogen levels, and blood urea nitrogen. This model exhibited good discriminative ability, with a C‐index value of 0.788 (95% confidence interval [CI]: 0.742–0.833) in the training cohort and 0.888 (95% CI: 0.835–0.941) in the testing cohort; it was well‐calibrated in both cohorts. Decision curve analyses supported the clinical value of this predictive nomogram when used at a lung cancer possibility threshold of 18%. Ten‐fold cross‐validation indicated good stability and accuracy of the model (kappa = 0.416 ± 0.128; accuracy = 0.751 ± 0.056; area under the curve = 0.768 ± 0.049). Conclusion Our risk model can reasonably predict the risks of lung cancer in nonsmoking Chinese patients with lung nodules.

Background: Lung cancer is a major global threat to public health for which a novel prognostic nomogram is urgently needed.Patients and methods: Here, we designed a novel prognostic nomogram using a training dataset consisting of 178 pulmonary nodules for design and 124nodules for external validation. The R ‘caret’ package was used to separate patients for design into two groups, including a training cohort (n=126) for model construction and an internal validation cohort (n=52). Optimal feature selection for this model was achieved using the least absolute shrinkage and selection operator regression (LASSO) model. C-index values, calibration plots, and decision curve analyses were used to gauge the discrimination, calibration, and clinical utility, respectively, of this predictive model. Validation was then performed with the validation cohort.Results: A predictive nomogram was successfully constructed incorporating hypertension status, plasma fibrinogen levels, serum uric acid (SUA) levels, triglyceride (TG) and high-density lipoprotein (HDL) levels, density, spicule sign, ground-glass opacity (GGO), and pulmonary nodule size. This model exhibited good discriminative ability, with a C-index value of 0.795 (95% CI: 0.720–0.870), and was well-calibrated. When we used the validation cohort to evaluate the model, the C-indexes were 0.886 (95% CI: 0.800–0.972) and 0.817 (95% CI: 0.747–0.897) for internal validation and external validation, respectively. Decision curve analyses indicated the clinical value of this predictive nomogram when used at a lung cancer possibility threshold of 9%.Conclusion: The nomogram constructed in this study, which incorporates hypertension status, plasma fibrinogen levels, SUA, TG, HDL, density, spicule sign, GGO status, and pulmonary nodule size was able to reliably predict lung cancer risk in this Chinese cohort of patients presenting with pulmonary nodules.