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

Emmy Borgmästars; Sara Jacobson; Maja Simm; Mattias Johansson; Ola Billing; Christina Lundin; Hanna Nyström; Daniel Öhlund; Zelmina Lubovac-Pilav; Pär Jonsson; Oskar Franklin; Malin Sund

doi:10.21037/jgo-23-930

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

J Gastrointest Oncol. 2024 Apr 30;15(2):755-767. doi: 10.21037/jgo-23-930. Epub 2024 Apr 28.

Authors

Emmy Borgmästars¹, Sara Jacobson¹, Maja Simm^{1

2}, Mattias Johansson³, Ola Billing¹, Christina Lundin¹, Hanna Nyström^{1

4}, Daniel Öhlund^{4

5}, Zelmina Lubovac-Pilav⁶, Pär Jonsson⁷, Oskar Franklin^#^{1

8}, Malin Sund^#^{1

9}

Affiliations

¹ Department of Surgical and Perioperative Sciences/Surgery, Umeå University, Umeå, Sweden.
² Department of Clinical Sciences/Obstetrics and Gynecology, Umeå University, Umeå, Sweden.
³ Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
⁴ Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden.
⁵ Department of Radiation Sciences/Oncology, Umeå University, Umeå, Sweden.
⁶ Department of Biology and Bioinformatics, University of Skövde, Skövde, Sweden.
⁷ Department of Chemistry, Umeå University, Umeå, Sweden.
⁸ Division of Surgical Oncology, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA.
⁹ Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.

^# Contributed equally.

Abstract

Background: Pancreatic ductal adenocarcinoma (pancreatic cancer) is often detected at late stages resulting in poor overall survival. To improve survival, more patients need to be diagnosed early when curative surgery is feasible. We aimed to identify circulating metabolites that could be used as early pancreatic cancer biomarkers.

Methods: We performed metabolomics by liquid and gas chromatography-mass spectrometry in plasma samples from 82 future pancreatic cancer patients and 82 matched healthy controls within the Northern Sweden Health and Disease Study (NSHDS). Logistic regression was used to assess univariate associations between metabolites and pancreatic cancer risk. Least absolute shrinkage and selection operator (LASSO) logistic regression was used to design a metabolite-based risk score. We used receiver operating characteristic (ROC) analyses to assess the discriminative performance of the metabolite-based risk score.

Results: Among twelve risk-associated metabolites with a nominal P value <0.05, we defined a risk score of three metabolites [indoleacetate, 3-hydroxydecanoate (10:0-OH), and retention index (RI): 2,745.4] using LASSO. A logistic regression model containing these three metabolites, age, sex, body mass index (BMI), smoking status, sample date, fasting status, and carbohydrate antigen 19-9 (CA 19-9) yielded an internal area under curve (AUC) of 0.784 [95% confidence interval (CI): 0.714-0.854] compared to 0.681 (95% CI: 0.597-0.764) for a model without these metabolites (P value =0.007). Seventeen metabolites were significantly associated with pancreatic cancer survival [false discovery rate (FDR) <0.1].

Conclusions: Indoleacetate, 3-hydroxydecanoate (10:0-OH), and RI: 2,745.4 were identified as the top candidate biomarkers for early detection. However, continued efforts are warranted to determine the usefulness of these metabolites as early pancreatic cancer biomarkers.

Keywords: Pancreatic neoplasms; biomarkers; hyperglycemia; risk; survival.