Cargando…

Accurate prediction of acute pancreatitis severity based on genome-wide cell free DNA methylation profiles

BACKGROUND: Patients with severe acute pancreatitis (SAP) have a high mortality, thus early diagnosis and interventions are critical for improving survival. However, conventional tests are limited in acute pancreatitis (AP) stratification. We aimed to assess AP severity by integrating the informativ...

Descripción completa

Detalles Bibliográficos
Autores principales: Sun, Hong-Wei, Dai, Sheng-Jie, Kong, Hong-Ru, Fan, Jie-Xiang, Yang, Fang-Yuan, Dai, Ju-Qing, Jin, Yue-Peng, Yu, Guan-Zhen, Chen, Bi-Cheng, Shi, Ke-Qing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8680202/
https://www.ncbi.nlm.nih.gov/pubmed/34915915
http://dx.doi.org/10.1186/s13148-021-01217-z
_version_ 1784616696327176192
author Sun, Hong-Wei
Dai, Sheng-Jie
Kong, Hong-Ru
Fan, Jie-Xiang
Yang, Fang-Yuan
Dai, Ju-Qing
Jin, Yue-Peng
Yu, Guan-Zhen
Chen, Bi-Cheng
Shi, Ke-Qing
author_facet Sun, Hong-Wei
Dai, Sheng-Jie
Kong, Hong-Ru
Fan, Jie-Xiang
Yang, Fang-Yuan
Dai, Ju-Qing
Jin, Yue-Peng
Yu, Guan-Zhen
Chen, Bi-Cheng
Shi, Ke-Qing
author_sort Sun, Hong-Wei
collection PubMed
description BACKGROUND: Patients with severe acute pancreatitis (SAP) have a high mortality, thus early diagnosis and interventions are critical for improving survival. However, conventional tests are limited in acute pancreatitis (AP) stratification. We aimed to assess AP severity by integrating the informative clinical measurements with cell free DNA (cfDNA) methylation markers. METHODS: One hundred and seventy-five blood samples were collected from 61 AP patients at multiple time points, plus 24 samples from healthy individuals. Genome-wide cfDNA methylation profiles of all samples were characterized with reduced representative bisulfite sequencing. Clinical blood tests covering 93 biomarkers were performed on AP patients within 24 h. SAP predication models were built based on cfDNA methylation and conventional blood biomarkers separately and in combination. RESULTS: We identified 565 and 59 cfDNA methylation markers informative for acute pancreatitis and its severity. These markers were used to develop prediction models for AP and SAP with area under the receiver operating characteristic of 0.92 and 0.81, respectively. Twelve blood biomarkers were systematically screened for a predictor of SAP with a sensitivity of 87.5% for SAP, and a specificity of 100% in mild acute pancreatitis, significantly higher than existing blood tests. An expanded model integrating 12 conventional blood biomarkers with 59 cfDNA methylation markers further improved the SAP prediction sensitivity to 92.2%. CONCLUSIONS: These findings have demonstrated that accurate prediction of SAP by the integration of conventional and novel blood molecular markers, paving the way for early and effective SAP intervention through a non-invasive rapid diagnostic test. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-021-01217-z.
format Online
Article
Text
id pubmed-8680202
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher BioMed Central
record_format MEDLINE/PubMed
spelling pubmed-86802022021-12-20 Accurate prediction of acute pancreatitis severity based on genome-wide cell free DNA methylation profiles Sun, Hong-Wei Dai, Sheng-Jie Kong, Hong-Ru Fan, Jie-Xiang Yang, Fang-Yuan Dai, Ju-Qing Jin, Yue-Peng Yu, Guan-Zhen Chen, Bi-Cheng Shi, Ke-Qing Clin Epigenetics Research BACKGROUND: Patients with severe acute pancreatitis (SAP) have a high mortality, thus early diagnosis and interventions are critical for improving survival. However, conventional tests are limited in acute pancreatitis (AP) stratification. We aimed to assess AP severity by integrating the informative clinical measurements with cell free DNA (cfDNA) methylation markers. METHODS: One hundred and seventy-five blood samples were collected from 61 AP patients at multiple time points, plus 24 samples from healthy individuals. Genome-wide cfDNA methylation profiles of all samples were characterized with reduced representative bisulfite sequencing. Clinical blood tests covering 93 biomarkers were performed on AP patients within 24 h. SAP predication models were built based on cfDNA methylation and conventional blood biomarkers separately and in combination. RESULTS: We identified 565 and 59 cfDNA methylation markers informative for acute pancreatitis and its severity. These markers were used to develop prediction models for AP and SAP with area under the receiver operating characteristic of 0.92 and 0.81, respectively. Twelve blood biomarkers were systematically screened for a predictor of SAP with a sensitivity of 87.5% for SAP, and a specificity of 100% in mild acute pancreatitis, significantly higher than existing blood tests. An expanded model integrating 12 conventional blood biomarkers with 59 cfDNA methylation markers further improved the SAP prediction sensitivity to 92.2%. CONCLUSIONS: These findings have demonstrated that accurate prediction of SAP by the integration of conventional and novel blood molecular markers, paving the way for early and effective SAP intervention through a non-invasive rapid diagnostic test. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-021-01217-z. BioMed Central 2021-12-16 /pmc/articles/PMC8680202/ /pubmed/34915915 http://dx.doi.org/10.1186/s13148-021-01217-z Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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
Sun, Hong-Wei
Dai, Sheng-Jie
Kong, Hong-Ru
Fan, Jie-Xiang
Yang, Fang-Yuan
Dai, Ju-Qing
Jin, Yue-Peng
Yu, Guan-Zhen
Chen, Bi-Cheng
Shi, Ke-Qing
Accurate prediction of acute pancreatitis severity based on genome-wide cell free DNA methylation profiles
title Accurate prediction of acute pancreatitis severity based on genome-wide cell free DNA methylation profiles
title_full Accurate prediction of acute pancreatitis severity based on genome-wide cell free DNA methylation profiles
title_fullStr Accurate prediction of acute pancreatitis severity based on genome-wide cell free DNA methylation profiles
title_full_unstemmed Accurate prediction of acute pancreatitis severity based on genome-wide cell free DNA methylation profiles
title_short Accurate prediction of acute pancreatitis severity based on genome-wide cell free DNA methylation profiles
title_sort accurate prediction of acute pancreatitis severity based on genome-wide cell free dna methylation profiles
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8680202/
https://www.ncbi.nlm.nih.gov/pubmed/34915915
http://dx.doi.org/10.1186/s13148-021-01217-z
work_keys_str_mv AT sunhongwei accuratepredictionofacutepancreatitisseveritybasedongenomewidecellfreednamethylationprofiles
AT daishengjie accuratepredictionofacutepancreatitisseveritybasedongenomewidecellfreednamethylationprofiles
AT konghongru accuratepredictionofacutepancreatitisseveritybasedongenomewidecellfreednamethylationprofiles
AT fanjiexiang accuratepredictionofacutepancreatitisseveritybasedongenomewidecellfreednamethylationprofiles
AT yangfangyuan accuratepredictionofacutepancreatitisseveritybasedongenomewidecellfreednamethylationprofiles
AT daijuqing accuratepredictionofacutepancreatitisseveritybasedongenomewidecellfreednamethylationprofiles
AT jinyuepeng accuratepredictionofacutepancreatitisseveritybasedongenomewidecellfreednamethylationprofiles
AT yuguanzhen accuratepredictionofacutepancreatitisseveritybasedongenomewidecellfreednamethylationprofiles
AT chenbicheng accuratepredictionofacutepancreatitisseveritybasedongenomewidecellfreednamethylationprofiles
AT shikeqing accuratepredictionofacutepancreatitisseveritybasedongenomewidecellfreednamethylationprofiles