Work

Pancreatic Cancer Detection and Differentiation from Chronic Pancreatitis: Potential Biomarkers Identified through a High-Throughput Multiplex Proteomic Assay and Machine Learning-Based Analysis

Year: 2025

Type: article

Abstract: Background: Pancreatic cancer (PC)-screening methods have limited accuracy despite their high clinical demand. Differential diagnosis of chronic pancreatitis (CP) poses another challenge for PC diagnosis. Therefore, we aimed to identify blood protein biomarkers for PC diagnosis and differential diagnosis of CP using high-throughput multiplex proteomic analysis. Methods: Two independent cohorts (N=88 and 80) were included, and residual serum samples were collected from all individuals (N=168). Each cohort consisted of four groups: healthy (H) individuals and those with CP, stage I/II PC (PC1), or stage III/IV PC (PC2). Protein expression in the first cohort was quantified using the Olink Immuno-Oncology and Oncology 3 proximity extension assay (PEA) panels and was analyzed using machine-learning (ML)-based analyses. Samples in the second cohort were utilized to verify candidate biomarkers in immunoassays. Results: Both the PEA and immunoassay results confirmed that previously recognized biomarkers, such as the mucin-16 and interleukin-6 proteins, were more highly expressed in the PC (PC1 and PC2) groups than in the non-PC (CP and H) groups. Several novel biomarkers for PC diagnosis were identified via ML-based feature extraction, including C1QA and CDHR2, whereas pro-neuropeptide Y (NPY) appeared to be a promising biomarker for the differential diagnosis of CP. Applying XGBoost classification incorporating the selected features resulted in an area under the curve of 0.92 (0.85-0.98) for differentiating the PC group from the CP and H groups. Conclusions: Promising blood biomarkers for PC diagnosis and differential diagnosis of CP were identified using a PEA platform and ML techniques.

Source: Annals of Laboratory Medicine

DOI: 10.3343/alm.2024.0492

Authors: Young-gon Kim, Sang-Mi Kim, Soo-Youn Lee

Institutions: Samsung Medical Center, Sungkyunkwan University, Chosun University, Chosun University Hospital

Language: English

Topic: Pancreatic and Hepatic Oncology Research

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: diamond

Funders: National Research Foundation of Korea, Ministry of Science and ICT, South Korea