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

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 b...

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Bibliographic Details
Main Authors: Francisco Carrillo-Perez, Octavio Caba, Cristina Jiménez-Luna, Francisco Ortuño, Daniel Castillo-Secilla, Luis Javier Herrera, Jose Prados, Ignacio Rojas
Format: Article
Language:English
Published: Elsevier 2025-07-01
Series:Heliyon
Subjects:
Machine learning
Pancreatic ductal adenocarcinoma
Chronic pancreatitis
Peripheral blood mononuclear cells
Clinical decision support systems
Transcriptomics
Online Access:http://www.sciencedirect.com/science/article/pii/S2405844025015191
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Summary: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.
ISSN:2405-8440

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