Journal Article Ppublish 3 citations
PubMed: 39018772 DOI: 10.1016/j.tranon.2024.102059
Multi-omics profiling to identify early plasma biomarkers in pre-diagnostic pancreatic ductal adenocarcinoma: a nested case-control study.
Transl Oncol 48:102059
Authors: Emmy Borgmästars, Benjamin Ulfenborg, Mattias Johansson, Pär Jonsson, Ola Billing, Oskar Franklin, Christina Lundin, Sara Jacobson, Maja Simm, Zelmina Lubovac-Pilav, Malin Sund
Affiliations
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden.
- School of Bioscience, Department of Biology and Bioinformatics, University of Skövde, Skövde, Sweden.
- Genomic Epidemiology Branch, International Agency for Research on Cancer, Lyon, France.
- Department of Chemistry, Umeå University, Umeå, Sweden.
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden.
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden; Division of Surgical Oncology, Department of Surgery, University of Colorado School of Medicine, Aurora, CO, USA.
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden; Department of Clinical Sciences/ Obstetrics and Gynecology, Umeå University, Umeå, Sweden.
- Department of Diagnostics and Intervention/ Surgery, Umeå University, Umeå, Sweden; Department of Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland.
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is an aggressive disease with poor survival. Novel biomarkers are urgently needed to improve the outcome through early detection. Here, we aimed to discover novel biomarkers for early PDAC detection using multi-omics profiling in pre-diagnostic plasma samples biobanked after routine health examinations. A nested case-control study within the Northern Sweden Health and Disease Study was designed. Pre-diagnostic plasma samples from 37 future PDAC patients collected within 2.3 years before diagnosis and 37 matched healthy controls were included. We analyzed metabolites using liquid chromatography mass spectrometry and gas chromatography mass spectrometry, microRNAs by HTG edgeseq, proteins by multiplex proximity extension assays, as well as three clinical biomarkers using milliplex technology. Supervised and unsupervised multi-omics integration were performed as well as univariate analyses for the different omics types and clinical biomarkers. Multiple hypothesis testing was corrected using Benjamini-Hochberg's method and a false discovery rate (FDR) below 0.1 was considered statistically significant. Carbohydrate antigen (CA) 19-9 was associated with PDAC risk (OR [95 % CI] = 3.09 [1.31-7.29], FDR = 0.03) and increased closer to PDAC diagnosis. Supervised multi-omics models resulted in poor discrimination between future PDAC cases and healthy controls with obtained accuracies between 0.429-0.500. No single metabolite, microRNA, or protein was differentially altered (FDR < 0.1) between future PDAC cases and healthy controls. CA 19-9 levels increase up to two years prior to PDAC diagnosis but extensive multi-omics analysis including metabolomics, microRNAomics and proteomics in this cohort did not identify novel early biomarkers for PDAC.
MeSH
Micrornas, Case-Control Studies, Adenocarcinoma, Multiple Chronic Conditions, Biomarkers, Diagnosis, Ca-19-9 Antigen, Plasma