Cargando…

Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies

BACKGROUND: Clinical parameter-based nomograms and staging systems provide limited information for the prediction of survival in intrahepatic cholangiocarcinoma (ICC) patients. In this study, we developed a methylation signature that precisely predicts overall survival (OS) after surgery. METHODS: A...

Descripción completa

Detalles Bibliográficos
Autores principales: Chen, Xing, Dong, Liangqing, Chen, Lu, Wang, Yuan, Du, Jinpeng, Ma, Lijie, Yan, Xiaokai, Huang, Jiwei, Liao, Mingheng, Chen, Xiangzheng, Liu, Dongming, Li, Jin, Zhang, Bo, Teng, Wen, Yuan, Kefei, Sun, Deqiang, Gao, Qiang, Zeng, Yong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10432305/
https://www.ncbi.nlm.nih.gov/pubmed/37601000
http://dx.doi.org/10.21037/hbsn-21-424
_version_ 1785091373156794368
author Chen, Xing
Dong, Liangqing
Chen, Lu
Wang, Yuan
Du, Jinpeng
Ma, Lijie
Yan, Xiaokai
Huang, Jiwei
Liao, Mingheng
Chen, Xiangzheng
Liu, Dongming
Li, Jin
Zhang, Bo
Teng, Wen
Yuan, Kefei
Sun, Deqiang
Gao, Qiang
Zeng, Yong
author_facet Chen, Xing
Dong, Liangqing
Chen, Lu
Wang, Yuan
Du, Jinpeng
Ma, Lijie
Yan, Xiaokai
Huang, Jiwei
Liao, Mingheng
Chen, Xiangzheng
Liu, Dongming
Li, Jin
Zhang, Bo
Teng, Wen
Yuan, Kefei
Sun, Deqiang
Gao, Qiang
Zeng, Yong
author_sort Chen, Xing
collection PubMed
description BACKGROUND: Clinical parameter-based nomograms and staging systems provide limited information for the prediction of survival in intrahepatic cholangiocarcinoma (ICC) patients. In this study, we developed a methylation signature that precisely predicts overall survival (OS) after surgery. METHODS: An epigenome-wide study of DNA methylation based on whole-genome bisulfite sequencing (WGBS) was conducted for two independent cohorts (discovery cohort, n=164; validation cohort, n=170) from three hepatobiliary centers in China. By referring to differentially methylated regions (DMRs), we proposed the concept of prognostically methylated regions (PMRs), which were composed of consecutive prognostically methylated CpGs (PMCs). Using machine learning strategies (Random Forest and the least absolute shrinkage and selector regression), a prognostic methylation score (PMS) was constructed based on 14 PMRs in the discovery cohort and confirmed in the validation cohort. RESULTS: The C-indices of the PMS for predicting OS in the discovery and validation cohorts were 0.79 and 0.74, respectively. In the whole cohort, the PMS was an independent predictor of OS [hazard ratio (HR) =8.12; 95% confidence interval (CI): 5.48–12.04; P<0.001], and the C-index (0.78) of the PMS was significantly higher than that of the Johns Hopkins University School of Medicine (JHUSM) nomogram (0.69, P<0.001), the Eastern Hepatobiliary Surgery Hospital (EHBSH) nomogram (0.67, P<0.001), American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (0.61, P<0.001), and MEGNA prognostic score (0.60, P<0.001). The patients in quartile 4 of PMS could benefit from adjuvant therapy (AT) (HR =0.54; 95% CI: 0.32–0.91; log-rank P=0.043), whereas those in the quartiles 1–3 could not. However, other nomograms and staging system failed to do so. Further analyses of potential mechanisms showed that the PMS was associated with tumor biological behaviors, pathway activation, and immune microenvironment. CONCLUSIONS: The PMS could improve the prognostic accuracy and identify patients who would benefit from AT for ICC patients, and might facilitate decisions in treatment of ICC patients.
format Online
Article
Text
id pubmed-10432305
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher AME Publishing Company
record_format MEDLINE/PubMed
spelling pubmed-104323052023-08-18 Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies Chen, Xing Dong, Liangqing Chen, Lu Wang, Yuan Du, Jinpeng Ma, Lijie Yan, Xiaokai Huang, Jiwei Liao, Mingheng Chen, Xiangzheng Liu, Dongming Li, Jin Zhang, Bo Teng, Wen Yuan, Kefei Sun, Deqiang Gao, Qiang Zeng, Yong Hepatobiliary Surg Nutr Original Article BACKGROUND: Clinical parameter-based nomograms and staging systems provide limited information for the prediction of survival in intrahepatic cholangiocarcinoma (ICC) patients. In this study, we developed a methylation signature that precisely predicts overall survival (OS) after surgery. METHODS: An epigenome-wide study of DNA methylation based on whole-genome bisulfite sequencing (WGBS) was conducted for two independent cohorts (discovery cohort, n=164; validation cohort, n=170) from three hepatobiliary centers in China. By referring to differentially methylated regions (DMRs), we proposed the concept of prognostically methylated regions (PMRs), which were composed of consecutive prognostically methylated CpGs (PMCs). Using machine learning strategies (Random Forest and the least absolute shrinkage and selector regression), a prognostic methylation score (PMS) was constructed based on 14 PMRs in the discovery cohort and confirmed in the validation cohort. RESULTS: The C-indices of the PMS for predicting OS in the discovery and validation cohorts were 0.79 and 0.74, respectively. In the whole cohort, the PMS was an independent predictor of OS [hazard ratio (HR) =8.12; 95% confidence interval (CI): 5.48–12.04; P<0.001], and the C-index (0.78) of the PMS was significantly higher than that of the Johns Hopkins University School of Medicine (JHUSM) nomogram (0.69, P<0.001), the Eastern Hepatobiliary Surgery Hospital (EHBSH) nomogram (0.67, P<0.001), American Joint Committee on Cancer (AJCC) tumor-node-metastasis (TNM) staging system (0.61, P<0.001), and MEGNA prognostic score (0.60, P<0.001). The patients in quartile 4 of PMS could benefit from adjuvant therapy (AT) (HR =0.54; 95% CI: 0.32–0.91; log-rank P=0.043), whereas those in the quartiles 1–3 could not. However, other nomograms and staging system failed to do so. Further analyses of potential mechanisms showed that the PMS was associated with tumor biological behaviors, pathway activation, and immune microenvironment. CONCLUSIONS: The PMS could improve the prognostic accuracy and identify patients who would benefit from AT for ICC patients, and might facilitate decisions in treatment of ICC patients. AME Publishing Company 2022-04-22 2023-08-01 /pmc/articles/PMC10432305/ /pubmed/37601000 http://dx.doi.org/10.21037/hbsn-21-424 Text en 2023 Hepatobiliary Surgery and Nutrition. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Chen, Xing
Dong, Liangqing
Chen, Lu
Wang, Yuan
Du, Jinpeng
Ma, Lijie
Yan, Xiaokai
Huang, Jiwei
Liao, Mingheng
Chen, Xiangzheng
Liu, Dongming
Li, Jin
Zhang, Bo
Teng, Wen
Yuan, Kefei
Sun, Deqiang
Gao, Qiang
Zeng, Yong
Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies
title Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies
title_full Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies
title_fullStr Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies
title_full_unstemmed Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies
title_short Epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies
title_sort epigenome-wide development and validation of a prognostic methylation score in intrahepatic cholangiocarcinoma based on machine learning strategies
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10432305/
https://www.ncbi.nlm.nih.gov/pubmed/37601000
http://dx.doi.org/10.21037/hbsn-21-424
work_keys_str_mv AT chenxing epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT dongliangqing epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT chenlu epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT wangyuan epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT dujinpeng epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT malijie epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT yanxiaokai epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT huangjiwei epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT liaomingheng epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT chenxiangzheng epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT liudongming epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT lijin epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT zhangbo epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT tengwen epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT yuankefei epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT sundeqiang epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT gaoqiang epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies
AT zengyong epigenomewidedevelopmentandvalidationofaprognosticmethylationscoreinintrahepaticcholangiocarcinomabasedonmachinelearningstrategies