Lung cancer is one of the most common malignant tumors in humans and the leading cause of cancer-related deaths worldwide. The microRNA-34 (miR-34) family is dysregulated in various human cancers and is an important family of tumor suppressor genes among microRNAs. The miR-34 family is downregulated in lung cancer. It inhibits cell proliferation, metastasis, and invasion, arrests the cell cycle, and induces apoptosis or senescence by negatively regulating many oncogenes. It is commonly used to detect and treat lung cancer. This study describes the regulatory role of the miR-34 family in lung cancer and the associated research advances in treatment.

Objective: This study aims to identify the most suitable machine-learning model for early heart disease risk screening in diabetic populations. Methods: This retrospective cohort study utilized data from the China Health and Retirement Longitudinal Study, with baseline data from 2011 and follow-up data from 2020. Using features selected by Least Absolute Shrinkage and Selection Operator (LASSO) regression, we systematically constructed 16 distinct machine-learning models. Model performance was evaluated using a comprehensive set of metrics, including the area under the receiver operating characteristic curve, F1-score, sensitivity, specificity, precision, accuracy, and balanced accuracy. To interpret the decision-making process of the best-performing model, we conducted Shapley additive explanations (SHAP) analysis. Results: After the 9-year follow-up period concluding in 2020, 157 of the 819 patients with diabetes at baseline (2011) developed heart disease. From the available features, LASSO regression selected 19 core features for model construction. Among the models developed, the K-Nearest Neighbors (KNN) model demonstrated optimal performance across key metrics, achieving the highest F1 score, balanced accuracy, and precision. The SHAP analysis identified body mass index, systolic blood pressure, and waist circumference as the three most important predictive features within the diabetic cohort. The contribution patterns of these features in the KNN model align closely with clinical expertise, achieving a strong balance between predictive power and interpretability. Conclusion: This study developed a machine-learning model to predict heart disease risk in patients with diabetes. Although the model exhibited only modest predictive performance, it provides a valuable empirical foundation and clear direction for constructing more reliable and clinically useful prediction tools in this field.