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Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. 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 pancreatic cancer patient samples from 14 hospitals and non-cancerous healthy control samples. We created ensemble models that combined automated machine learning with highly expressed miRNAs and their combination with CA and validated the performance of the models in the independent validation cohort.
The diagnostic model with the combination of the highly expressed miRNAs and CA could discriminate pancreatic cancer from non-cancer healthy control with high accuracy area under the curve AUC , 0. We validated high diagnostic accuracy in an independent asymptomatic early-stage stage 0-I pancreatic cancer cohort AUC We demonstrate that the highly expressed miRNAs and their combination with CA could be biomarkers for the specific and early detection of pancreatic cancer.
Pancreatic cancer is the 4th leading cause of cancer-related deaths worldwide and is projected to become the second most lethal tumor by [ 1 ]. Therefore, early detection of pancreatic cancer without local invasion or distant metastasis is critical for improving the survival of patients with pancreatic cancer.