Abstract
Pancreatic Ductal Adenocarcinoma (PDAC) is one of the most lethal cancers, accounting for a significant proportion of cancer-related deaths globally. Despite advancements in medical science, treatment options for PDAC remain limited, and the prognosis is often poor. Early detection is a critical factor in improving patient outcomes, but current diagnostic methods often fail to detect PDAC until it has advanced to a late stage. In this context, the development of more effective diagnostic tools is of paramount importance. In this study, we explored the potential of non-coding RNAs (ncRNAs) as diagnostic markers for PDAC using cell-free nucleotides and liquid biopsies. Leveraging the power of Next Generation Sequencing (NGS), bioinformatics analysis, and machine learning (ML), we were able to identify unique RNA signatures associated with PDAC. Our findings revealed twenty key genes, including microRNAs (miRNAs), long-non-coding RNAs (lncRNAs), and miscellaneous RNAs that demonstrated high classification accuracy. Specifically, our model achieved a classification accuracy of 87% and an area under the receiver operating characteristic curve (AUC) of 91%. These ncRNAs could potentially serve as robust biomarkers for PDAC, offering a promising avenue for the development of a non-invasive diagnostic test. This could revolutionize PDAC diagnosis, enabling earlier detection and intervention, which is crucial for improving patient outcomes. This work lays the groundwork for future research, with the potential to significantly enhance PDAC diagnosis and therapy.