Noninvasive Detection of Pancreatic Ductal Adenocarcinoma in High-Risk Patients Using miRNA from Urinary Extracellular Vesicles

Tomoya Kawase¹Yasutaka Kato²Hiroshi Nishihara²Shogo Baba³Tadatoshi Kawasaki³Hiroshi Kurahara⁴Hideyuki Oi⁴Shunsuke Kondo⁵Mao Okada⁵Tomoyuki Satake⁵Yukiko Shimoda Igawa⁵Tatsuya Yoshida⁵Junji Kita⁶Johji Imura⁶Kazuya Kinoshita⁶Masaya Yokoyama⁶Atsushi Satomura⁷Kazuya Takayama⁷Motoki Mikami⁷Yumi Nishiyama⁷Mika Mizunuma⁷Yuki Ichikawa⁷Koji Yoshida^1*

• ¹Kawasaki Ika Daigaku, Kurashiki, Japan
• ²Keio University School of Medicine, Tokyo, Japan
• ³Hokuto Byoin, Obihiro, Japan
• ⁴Kagoshima Daigaku, Kagoshima, Japan
• ⁵National Cancer Center Hospital, Tokyo, Japan
• ⁶Iryo Hojin Kumagaya Sogo Byoin, Kumagaya, Japan
• ⁷Craif Inc., Tokyo, Japan

Pancreatic cancer (PaC), which is characterized by a high mortality rate, is often diagnosed at an advanced stage, significantly limiting treatment effectiveness. Early detection is crucial for improving survival rates, especially for individuals at high risk (HR) for PaC. Traditional diagnostic methods, including ultrasound, computed tomography, and magnetic resonance imaging (MRI), have limited sensitivity, especially for detecting early-stage PaC. We explored the potential of miRNA from urinary extracellular vesicles (EVs) as a noninvasive diagnostic marker for PaC. An exploratory case–control study was conducted across multiple Japanese institutions. The study included 248 samples from patients with pancreatic ductal adenocarcinoma (PDAC), the most common type of PaC, and HR patients. Differential expression analysis revealed significant differences in 16 miRNAs between the PDAC and HR samples. A machine learning-based algorithm was developed based on these miRNAs to distinguish between PDAC and HR. The algorithm exhibited an AUC of 0.89, a sensitivity of 0.80, and a specificity of 0.79. The algorithm detected the early-stage PDAC (stage 0-IIA) with a sensitivity of 0.73. These findings highlight the potential of the urinary miRNA algorithm as a noninvasive tool to aid in the detection of PDAC, including early-stage cases, in high-risk populations.