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Circulating tissue polypeptide-specific antigen in pre-diagnostic pancreatic cancer samples
Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.ORCID iD: 0000-0001-9521-4463
Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.ORCID iD: 0000-0001-7426-6889
Umeå University, Faculty of Medicine, Wallenberg Centre for Molecular Medicine at Umeå University (WCMM). Umeå University, Faculty of Medicine, Department of Radiation Sciences, Oncology.ORCID iD: 0000-0002-5847-2778
Umeå University, Faculty of Medicine, Wallenberg Centre for Molecular Medicine at Umeå University (WCMM). Umeå University, Faculty of Medicine, Department of Surgical and Perioperative Sciences, Surgery.
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2021 (English)In: Cancers, ISSN 2072-6694, Vol. 13, no 21, article id 5321Article in journal (Refereed) Published
Abstract [en]

Early detection of pancreatic ductal adenocarcinoma (PDAC) is challenging, and late diagnosis partly explains the low 5-year survival. Novel and sensitive biomarkers are needed to enable early PDAC detection and improve patient outcomes. Tissue polypeptide specific antigen (TPS) has been studied as a biomarker in PDAC diagnostics, and it has previously been shown to reflect clinical status better than the ‘golden standard’ biomarker carbohydrate antigen 19-9 (CA 19-9) that is most widely used in the clinical setting. In this cross-sectional case-control study using pre-diagnostic plasma samples, we aim to evaluate the potential of TPS as a biomarker for early PDAC detection. Furthermore, in a subset of individuals with multiple samples available at different time points before diagnosis, a longitudinal analysis was used. We assessed plasma TPS levels using enzyme-linked immunosorbent assay (ELISA) in 267 pre-diagnostic PDAC plasma samples taken up to 18.8 years before clinical PDAC diagnosis and in 320 matched healthy controls. TPS levels were also assessed in 25 samples at PDAC diagnosis. Circulating TPS levels were low both in pre-diagnostic samples of future PDAC patients and in healthy controls, whereas TPS levels at PDAC diagnosis were significantly increased (odds ratio 1.03; 95% confidence interval: 1.01–1.05) in a logistic regression model adjusted for age. In conclusion, TPS levels increase late in PDAC progression and hold no potential as a biomarker for early detection.

Place, publisher, year, edition, pages
MDPI, 2021. Vol. 13, no 21, article id 5321
Keywords [en]
Circulating biomarkers, Early detection, Pancreatic ductal adenocarcinoma, Pre-diagnostic cohort, TPS
National Category
Cancer and Oncology Gastroenterology and Hepatology
Identifiers
URN: urn:nbn:se:umu:diva-188952DOI: 10.3390/cancers13215321ISI: 000720278600001Scopus ID: 2-s2.0-85117441860OAI: oai:DiVA.org:umu-188952DiVA, id: diva2:1607033
Funder
Region VästerbottenSjöberg FoundationSwedish Cancer Society, 19 0273, 2017-557, CAN 2017/332, CAN 2017/827Knut and Alice Wallenberg Foundation, RV-549731, RV-583411, RV-841551, RV-930167, VLL-643451Swedish Research Council, 2016-02990, 2017-01531, 2019-01690Available from: 2021-10-29 Created: 2021-10-29 Last updated: 2023-09-05Bibliographically approved
In thesis
1. In search of early biomarkers in pancreatic ductal adenocarcinoma using multi-omics and bioinformatics
Open this publication in new window or tab >>In search of early biomarkers in pancreatic ductal adenocarcinoma using multi-omics and bioinformatics
2022 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
På jakt efter tidiga biomarkörer i bukspottkörtelcancer med hjälp av multi-omik och bioinformatik
Abstract [en]

Background: Pancreatic ductal adenocarcinoma (PDAC) is a very aggressive malignancy with a 5-year survival of 10 %. Surgery is the only curative treatment. Unfortunately, few patients are eligible for surgery due to late detection. Thus, we need ways to detect the disease at an earlier stage and for that good screening biomarkers could be used. Previous studies have analyzed circulating analytes in prospective studies to identify early PDAC signals. One such class is microRNAs (miRNAs). MicroRNAs are non-coding RNAs of around 22 nucleotides that act as post- transcriptional regulators by interaction with messenger RNAs (mRNAs). The function of a miRNA can be elucidated by target prediction, to identify its potential targets, followed by enrichment analysis of the predicted targets. Challenges with this approach includes a lot of false positives being generated and that miRNAs can perform their role in a tissue- or disease-specific manner. Other classes of analytes that have previously been studied in prospective PDAC cohorts are metabolites and proteins. 

Aims: This thesis has three aims. First, to build a miRNA functional analysis pipeline with correlation support between miRNA and its predicted target genes. Second, to identify potential circulating biomarkers for early detection of PDAC using multi-omics. Third, to identify potential prognostic metabolites in a prospective PDAC cohort.

Methods: We used publicly available data from the cancer genome atlas-pancreatic adenocarcinoma (TCGA-PAAD) and pre-diagnostic plasma samples from the Northern Sweden Health and Disease Study. We built a pipeline in R including miRNA, mRNA, and protein expression data from TCGA-PAAD for in silico miRNA functional analysis. Pre- diagnostic plasma samples from future PDAC patients as well as matched healthy controls were analyzed using multi- omics. Tissue polypeptide specific antigen (TPS) was analyzed by enzyme linked immunosorbent assay in 267 future PDAC samples and 320 healthy controls. Metabolomics and clinical biomarkers (carbohydrate antigen (CA) 19-9, carcinoembryonic antigen (CEA), and CA 15-3) were profiled in 100 future PDAC samples and 100 healthy controls using liquid chromatography-mass spectrometry (MS), gas chromatography-MS, and multi-plex technology. Of these, a subset of 39 future PDAC patients and 39 healthy controls were profiled for 2083 microRNAs using targeted sequencing and 644 proteins using proximity extension assays. Circulating levels of multi-omics analytes were analyzed using conditional or unconditional logistic regression. Least absolute shrinkage and selection operator (LASSO) in combination with 500 bootstrap iterations identified the most informative variables. The prognostic value of metabolites was assessed using cox regression. Multi-omics factor analysis (MOFA) and data integration analysis for biomarker discovery using latent components (DIABLO) were used for multi-omics integration analyses.

Results: An automated pipeline was built consisting of 1) miRNA target prediction, 2) correlation analyses between miRNA and its targets on mRNA and protein expression levels, and 3) functional enrichment of correlated targets to identify enriched Kyoto encyclopedia of genes and genomes (KEGG) pathways and gene ontology (GO) terms for a specific miRNA. The pipeline was run for all microRNAs (~700) detected in the TCGA-PAAD cohort. These results can be downloaded from a shiny app (https://emmbor.shinyapps.io/mirfa/). TPS was not altered in pre-diagnostic PDAC patients up to 24 years prior to diagnosis, but increased at diagnosis (OR = 1.03, 95 % CI: 1.01-1.05). Internal area under curves of 0.74, 0.80, and 0.88 were achieved for five metabolites, two proteins, and two miRNAs that were selected by LASSO and bootstrap iterations, in combination with CA 19-9. Neither MOFA nor DIABLO separated well between future PDAC cases and healthy controls. 

Conclusions: Our bioinformatics pipeline for in silico functional analysis of microRNAs successfully identifies enriched KEGG pathways and GO terms for miRNA isoforms. The investigated plasma samples are heterogeneous, but among the analyzed variables, we identified five metabolites, two proteins, and two microRNAs with highest potential for early PDAC detection. CA 19-9 levels increased closer to diagnosis. We identified five fatty acids that could be studied in a diagnostic PDAC cohort as prognostic biomarkers. 

Place, publisher, year, edition, pages
Umeå: Umeå University, 2022. p. 69
Series
Umeå University medical dissertations, ISSN 0346-6612 ; 2211
Keywords
Pancreatic cancer, microRNA functional analysis, TPS, miRNomics, metabolomics, proteomics, plasma samples, early detection, risk, survival
National Category
Cancer and Oncology Bioinformatics and Systems Biology
Identifiers
urn:nbn:se:umu:diva-201158 (URN)978-91-7855-929-9 (ISBN)978-91-7855-928-2 (ISBN)
Public defence
2022-12-16, Hörsal B Norrlands universitetssjukhus, Unod T9, Norrlands universitetssjukhus, Umeå, 13:00 (English)
Opponent
Supervisors
Available from: 2022-11-25 Created: 2022-11-22 Last updated: 2022-11-24Bibliographically approved

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Borgmästars, EmmyLundberg, ErikÖhlund, DanielNyström, HannaFranklin, OskarLundin, ChristinaJonsson, PärSund, Malin

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