Detection of Pancreatic Ductal Adenocarcinoma by A qPCR-based Normalizer-free Circulating Extracellular Vesicles RNA Signature
Da Qin, Yu Zhao, Qingdong Guo, Shengtao Zhu, Shutian Zhang, Li Min
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) is difficult to diagnose and many efforts have been made to evaluate EVs-derived RNAs as biomarkers to predict PDAC. However, lack of robust internal references largely limited their clinical application. Here we proposed an RNA ratio-based, normalizer-free algorithm to quantitate EVs-derived RNAs in PDAC. Methods: Differentially expressed RNAs in the training group were identified using "limma" package. The ratio of any two candidate RNAs in the same sample was calculated and used as a new biomarker. LASSO regression was performed to build prediction models based on those RNA ratios. RNA-seq data of 116 plasma samples and RT-qPCR data of 111 plasma samples were used for internal and external validation, separately. Three algorithms (lasso regression, logistic regression, and SVM) were compared to improve the performance of this RNA signature.