Machine learning identifies 6-gene signature in peripheral blood for pancreatic cancer diagnosis

Pancreatic ductal adenocarcinoma (PDAC) is associated with a poor prognosis, primarily due to late-stage detection.This underscores the critical need for informative biomarkers enabling earlier diagnosis and improved patient outcomes.This study leveraged machine learning techniques to identify a biologically relevant gene signature for accurately differentiating PDAC, chronic pancreatitis (CP), and healthy controls using blood-based RNA sequencing data.We analyzed two distinct datasets: extracellular vesicle long RNA (exLR) and peripheral blood mononuclear cell (PBMC) RNA-Seq.Feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm, followed by support vector machine (SVM) classification, identified a 15-gene signature derived from the exLR data.This signature successfully classified PDAC, CP, and healthy controls in both the exLR and PBMC datasets, achieving an F1-score of approximately 80 %.Further refinement yielded a 6-gene subset with established biological relevance to PDAC, which maintained strong classification performance (F1-score: 71.0 % in Leave-One-Out cross-validation).This study proposes a promising, biologically relevant gene signature derived from blood samples for the accurate, non-invasive differentiation of PDAC and CP, potentially facilitating earlier diagnosis and improving patient prognosis.