胰腺癌通常在晚期才被诊断出来，由于非特异性症状和缺乏可用的生物标志物，胰腺癌的早期诊断很困难。我们对来自 14 家医院的 212 名胰腺癌患者样本和 213 名非癌症健康人样本进行了全面的血清 miRNA 测序。控制样品。我们将胰腺癌和对照样本随机分为两个队列：训练队列 (N = 185) 和验证队列 (N = 240)。我们创建了将自动化机器学习与 100 个高表达 miRNA 及其与 CA19-9 组合相结合的集成模型，并在独立验证队列中验证了模型的性能。100 个高表达 miRNA 与 CA19-9 组合的诊断模型能够以高精度区分胰腺癌和非癌症健康对照（曲线下面积（AUC），0.99；敏感性，90%；特异性，98%）。我们在独立的无症状早期（0-I 期）胰腺癌队列中验证了高诊断准确性（AUC：0.97；敏感性，67%；特异性，98%）。我们证明 100 个高表达的 miRNA 及其与 CA19 的组合-9 可以作为胰腺癌特异性和早期检测的生物标志物。© 2024。作者。

Pancreatic cancer is often diagnosed at advanced stages, and early-stage diagnosis of pancreatic cancer is difficult because of nonspecific symptoms and lack of available biomarkers.We performed comprehensive serum miRNA sequencing of 212 pancreatic cancer patient samples from 14 hospitals and 213 non-cancerous healthy control samples. We randomly classified the pancreatic cancer and control samples into two cohorts: a training cohort (N = 185) and a validation cohort (N = 240). We created ensemble models that combined automated machine learning with 100 highly expressed miRNAs and their combination with CA19-9 and validated the performance of the models in the independent validation cohort.The diagnostic model with the combination of the 100 highly expressed miRNAs and CA19-9 could discriminate pancreatic cancer from non-cancer healthy control with high accuracy (area under the curve (AUC), 0.99; sensitivity, 90%; specificity, 98%). We validated high diagnostic accuracy in an independent asymptomatic early-stage (stage 0-I) pancreatic cancer cohort (AUC:0.97; sensitivity, 67%; specificity, 98%).We demonstrate that the 100 highly expressed miRNAs and their combination with CA19-9 could be biomarkers for the specific and early detection of pancreatic cancer.© 2024. The Author(s).