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Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort

BACKGROUND: Pancreatic cancer is a lethal disease with a high mortality rate. The difficulty of early diagnosis is one of its primary causes. Therefore, we aimed to discover non-invasive biomarkers that facilitate the early diagnosis of pancreatic cancer risk. METHODS: The study subjects were random...

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Autores principales: Han, Youngmin, Jung, Keum Ji, Kim, Unchong, Jeon, Chan Il, Lee, Kwangbae, Jee, Sun Ha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694897/
http://dx.doi.org/10.1186/s12967-023-04670-x
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author Han, Youngmin
Jung, Keum Ji
Kim, Unchong
Jeon, Chan Il
Lee, Kwangbae
Jee, Sun Ha
author_facet Han, Youngmin
Jung, Keum Ji
Kim, Unchong
Jeon, Chan Il
Lee, Kwangbae
Jee, Sun Ha
author_sort Han, Youngmin
collection PubMed
description BACKGROUND: Pancreatic cancer is a lethal disease with a high mortality rate. The difficulty of early diagnosis is one of its primary causes. Therefore, we aimed to discover non-invasive biomarkers that facilitate the early diagnosis of pancreatic cancer risk. METHODS: The study subjects were randomly selected from the Korean Cancer Prevention Study-II and matched by age, sex, and blood collection point [pancreatic cancer incidence (n = 128) vs. control (n = 256)]. The baseline serum samples were analyzed by non-targeted metabolomics, and XGBoost was used to select significant metabolites related to pancreatic cancer incidence. Genomewide association study for the selected metabolites discovered valuable single nucleotide polymorphisms (SNPs). Moderation and mediation analysis were conducted to explore the variables related to pancreatic cancer risk. RESULTS: Eleven discriminant metabolites were selected by applying a cut-off of 4.0 in XGBoost. Five SNP presented significance in metabolite-GWAS (p ≤ 5 × 10(–6)) and logistic regression analysis. Among them, the pair metabolite of rs2370981, rs55870181, and rs72805402 displayed a different network pattern with clinical/biochemical indicators on comparison with allelic carrier and non-carrier. In addition, we demonstrated the indirect effect of rs59519100 on pancreatic cancer risk mediated by γ-glutamyl tyrosine, which affects the smoking status. The predictive ability for pancreatic cancer on the model using five SNPs and four pair metabolites with the conventional risk factors was the highest (AUC: 0.738 [0.661–0.815]). CONCLUSIONS: Signatures involving metabolites and SNPs discovered in the present research may be closely associated with the pathogenesis of pancreatic cancer and for use as predictive biomarkers allowing early pancreatic cancer diagnosis and therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04670-x.
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spelling pubmed-106948972023-12-05 Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort Han, Youngmin Jung, Keum Ji Kim, Unchong Jeon, Chan Il Lee, Kwangbae Jee, Sun Ha J Transl Med Research BACKGROUND: Pancreatic cancer is a lethal disease with a high mortality rate. The difficulty of early diagnosis is one of its primary causes. Therefore, we aimed to discover non-invasive biomarkers that facilitate the early diagnosis of pancreatic cancer risk. METHODS: The study subjects were randomly selected from the Korean Cancer Prevention Study-II and matched by age, sex, and blood collection point [pancreatic cancer incidence (n = 128) vs. control (n = 256)]. The baseline serum samples were analyzed by non-targeted metabolomics, and XGBoost was used to select significant metabolites related to pancreatic cancer incidence. Genomewide association study for the selected metabolites discovered valuable single nucleotide polymorphisms (SNPs). Moderation and mediation analysis were conducted to explore the variables related to pancreatic cancer risk. RESULTS: Eleven discriminant metabolites were selected by applying a cut-off of 4.0 in XGBoost. Five SNP presented significance in metabolite-GWAS (p ≤ 5 × 10(–6)) and logistic regression analysis. Among them, the pair metabolite of rs2370981, rs55870181, and rs72805402 displayed a different network pattern with clinical/biochemical indicators on comparison with allelic carrier and non-carrier. In addition, we demonstrated the indirect effect of rs59519100 on pancreatic cancer risk mediated by γ-glutamyl tyrosine, which affects the smoking status. The predictive ability for pancreatic cancer on the model using five SNPs and four pair metabolites with the conventional risk factors was the highest (AUC: 0.738 [0.661–0.815]). CONCLUSIONS: Signatures involving metabolites and SNPs discovered in the present research may be closely associated with the pathogenesis of pancreatic cancer and for use as predictive biomarkers allowing early pancreatic cancer diagnosis and therapy. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12967-023-04670-x. BioMed Central 2023-12-04 /pmc/articles/PMC10694897/ http://dx.doi.org/10.1186/s12967-023-04670-x Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Han, Youngmin
Jung, Keum Ji
Kim, Unchong
Jeon, Chan Il
Lee, Kwangbae
Jee, Sun Ha
Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort
title Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort
title_full Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort
title_fullStr Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort
title_full_unstemmed Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort
title_short Non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the KCPS-II cohort
title_sort non-invasive biomarkers for early diagnosis of pancreatic cancer risk: metabolite genomewide association study based on the kcps-ii cohort
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10694897/
http://dx.doi.org/10.1186/s12967-023-04670-x
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