Metabolomics for early pancreatic cancer detection in plasma samples from a Swedish prospective population-based...

Area of Science:

• Metabolomics
• Biomarker Discovery
• Oncology

Background:

• Pancreatic ductal adenocarcinoma (pancreatic cancer) is frequently diagnosed at late stages, leading to poor survival rates.
• Early detection through surgical intervention is crucial for improving patient outcomes.
• Identifying reliable early biomarkers for pancreatic cancer is a significant unmet clinical need.

Purpose of the Study:

• To identify circulating metabolites that can serve as early diagnostic biomarkers for pancreatic cancer.
• To develop a metabolite-based risk score for predicting pancreatic cancer risk.

Main Methods:

• Plasma samples from 82 future pancreatic cancer patients and 82 matched controls were analyzed using liquid and gas chromatography-mass spectrometry.
• Logistic regression and LASSO logistic regression were employed to identify risk-associated metabolites and construct a risk score.
• Receiver operating characteristic (ROC) analyses were performed to evaluate the discriminative performance of the metabolite-based risk score.

Main Results:

• A risk score comprising indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 was developed using LASSO regression.
• A logistic regression model incorporating these three metabolites, clinical factors, and CA 19-9 achieved an AUC of 0.784, significantly outperforming a model without the metabolites (AUC = 0.681).
• Seventeen metabolites showed a significant association with pancreatic cancer survival (FDR < 0.1).

Conclusions:

• Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 are identified as top candidate biomarkers for the early detection of pancreatic cancer.
• The developed metabolite-based risk score demonstrates potential for improving early diagnostic capabilities.
• Further research is necessary to validate the clinical utility of these metabolites as early pancreatic cancer biomarkers.