Diagnostic and prognosis of benign and malignant early pulmonary nodules using serum miR-500a-3p combined with CT signs.

Accurate differentiation and risk assessment of pulmonary nodules are central to clinical management, but precise tools are still limited, and delays in distinguishing benign from malignant nodules can lead to unnecessary procedures or missed diagnoses, highlighting the urgent need for reliable biomarkers. This study included 114 patients with pathologically confirmed pulmonary nodules, from whom baseline clinical data, serum samples, and CT imaging were collected, and a diagnostic model integrating miR-500a-3p, nodule density, and diameter was established. In vitro, lung cancer cells were transfected with miR-500a-3p mimics to assess proliferation, migration, and invasion via CCK-8 and Transwell assays, and luciferase reporter assays were performed to validate the direct target of miR-500a-3p. Serum miR-500a-3p expression was lower in malignant nodules than in benign nodules, and a model incorporating miR-500a-3p, nodule density, and diameter demonstrated diagnostic accuracy (AUC = 0.887). High miR-500a-3p expression predicted longer progression-free survival, whereas low expression and large diameter were independent adverse prognostic factors, and the integrated model demonstrated concordance with pathological findings (Kappa = 0.636). In vitro, miR-500a-3p was markedly downregulated in lung cancer cell lines, and its overexpression significantly inhibited cell proliferation, migration, and invasion, while luciferase reporter assays confirmed that AFF4 is a direct target of miR-500a-3p. Serum miR-500a-3p aids in distinguishing malignant pulmonary nodules and predicting prognosis, and combining it with CT features enhances diagnostic accuracy; in vitro experiments confirmed that miR-500a-3p suppresses lung cancer cell proliferation, migration, and invasion and directly targets AFF4. Clinically, this model enables noninvasive risk stratification to guide -surgical decisions and follow-up planning, thereby improving -personalized patient management.