Pancreatic cancer (PC) is a highly aggressive malignancy in humans, where early diagnosis significantly improves patient outcomes. However, effective methods for accurate and early detection remain limited. In this multiethnic study involving human subjects, we developed a liquid biopsy signature based on extracellular vesicle (EV)-derived microRNAs (miRNAs) linked to radiomics features extracted from patients’ tumor imaging. We integrated eight datasets containing clinical records, imaging data of benign and malignant pancreatic lesions, and small RNA sequencing data from plasma-derived EVs of PC patients. Radiomics features were extracted and analyzed using the limma package, with feature selection conducted via the Boruta algorithm and model construction through Least Absolute Shrinkage and Selection Operator regression. Radiomics-related low-abundance EV miRNAs were identified via weighted gene co-expression network analysis and validated for diagnostic accuracy using 10 machine-learning algorithms. Three key EV miRNAs were found to robustly distinguish malignant from benign lesions. Subsequent molecular clustering of these miRNAs and their predicted targets identified two PC subtypes, with distinct survival profiles and therapeutic responses. Specifically, one cluster was associated with prolonged overall survival and higher predicted sensitivity to immunotherapy, while the other indicated high-risk tumors potentially amenable to targeted drug interventions. This radiogenomic EV miRNA signature in human plasma represents a promising non-invasive biomarker for early diagnosis and molecular subtyping of PC, with potential implications for precision treatment strategies.
Xu et al. (Mon,) studied this question.
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