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...
Autores principales: | , , , , , , , , , , , , , , , , , |
---|---|
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 |