Development of a diagnostic model for pancreatic ductal adenocarcinoma using machine learning and blood-based miRNAs.

Abstract

Introduction: Pancreatic ductal adenocarcinoma (PDAC) has the lowest survival rate among all major cancers due to a lack of symptoms in early stages, early detection tools, and optimal therapies for late-stage patients. Thus, an early diagnosis of PDAC is critical. Recently, circulating miRNAs have been reported to be altered in PDAC. They are promising biomarkers because of stability in the blood, ease of non-invasive detection, and convenient screening methods. This study aims to use blood-based miRNA biomarkers and various analysis methods in the development of a machine-learning (ML) model for PDAC.

Methods: Blood-based miRNAs associated with PDAC were collected from open sources. miRNA sequences, targeted genes, and involved pathways were used to construct a set of descriptors for an ML model.

Results: Bioinformatics analysis revealed that most genes in pancreatic cancer and insulin signaling pathways were targeted by the PDAC-related miRNAs. The best performing ML model with the Random Forest classifier was able to achieve an accuracy of 88.4%. Model evaluations of an independent PDAC-associated miRNAs test set had 100% accuracy while non-cancer miRNAs had 52.4% accuracy, indicating specificity to PDAC.

Conclusions: Our results suggest an ML model developed using blood-based miRNA biomarkers' target gene, pathway, and sequence features could be implicated in PDAC diagnostics.