ArticleScientific reports2025
Serum microRNA-24-based nomogram predicts prognosis for patients with resected pancreatic cancer.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Pancreatic cancer (PCa) is one of the malignant tumors with an extremely poor prognosis. Rare biomarkers exist for predicting the outcomes of PCa patients. This study aimed to develop a nomogram model based on serum microRNA-24 (miR-24) and clinicopathological factors to predict overall survival (OS) and treatment response to conventional adjuvant chemotherapy (ACT) in patients with PCa. This retrospective study included 296 patients with PCa who underwent radical resection and were followed up every three months. The serum levels of miR-24 were analyzed with real- time polymerase chain reaction, and the clinicopathological information relevant to the patients was extracted from the medical center. By combining miR-24 with some clinicopathological factors associated with prognosis, a nomogram model was developed to predict the OS of patients with PCa. Patients with elevated miR-24 levels exhibited significantly poorer OS compared to those at low risk (P < 0.0001). miR-24 was an independent predictor of OS regardless to the patients' age, gender, and clinical pathological characteristics. It demonstrated remarkable predictive power, with an AUC of 0.82, surpassing CA19-9 (AUC: 0.61), CA125 (AUC: 0.59), CA50 (AUC: 0.51) and CEA (0.56). When miR-24 was integrated with TNM stage, CA19-9 and CA125 in a nomogram, the prognostic accuracy was notably enhanced compared to individual factors. Furthermore, patients classified into the high-risk group who received post-operative ACT showed superior outcomes in both OS and two-year survival compared to those who did not receive ACT (P < 0.0001). A serum miR-24-based nomogram may serve as a powerful tool for predicting risk and prognosis in patients with resected pancreatic cancer, thus facilitating personalized clinical decision-making.
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