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Identification of DNA methylation biomarkers for risk of liver metastasis in early-stage colorectal cancer

BACKGROUND: Liver metastases can occur even in CRC patients who underwent curative surgery. While evidence suggested that adjuvant chemotherapy can help to reduce the occurrence of liver metastases for certain patients, it is not a recommended routine as the side effects outweigh the potential benef...

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Autores principales: Li, Weihua, Guo, Lei, Tang, Wanxiangfu, Ma, Yutong, Wang, Xiaonan, Shao, Yang, Zhao, Hong, Ying, Jianming
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190869/
https://www.ncbi.nlm.nih.gov/pubmed/34108011
http://dx.doi.org/10.1186/s13148-021-01108-3
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author Li, Weihua
Guo, Lei
Tang, Wanxiangfu
Ma, Yutong
Wang, Xiaonan
Shao, Yang
Zhao, Hong
Ying, Jianming
author_facet Li, Weihua
Guo, Lei
Tang, Wanxiangfu
Ma, Yutong
Wang, Xiaonan
Shao, Yang
Zhao, Hong
Ying, Jianming
author_sort Li, Weihua
collection PubMed
description BACKGROUND: Liver metastases can occur even in CRC patients who underwent curative surgery. While evidence suggested that adjuvant chemotherapy can help to reduce the occurrence of liver metastases for certain patients, it is not a recommended routine as the side effects outweigh the potential benefits, especially in Stage II CRC patients. This study aims to construct a model for predicting liver metastasis risk using differential methylation signals in primary CRC tumors, which can facilitate the decision for adjuvant chemotherapy. METHODS: Fifty-nine stage I/II and IV CRC patients were enrolled. Primary tumor, adjacent normal tissue, and metastatic tumor tissues were subject to targeted bisulfite sequencing for DNA methylation. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to identify potential DMRs for predicting liver metastasis of CRC. RESULTS: We identified a total of 241,573 DMRs by comparing the DNA methylation profile of primary tumors of stage II patients who developed metastasis to those who were metastasis-free during the follow up period. 213 DMRs were associated with poor prognosis, among which 182 DMRS were found to be hypermethylated in the primary tumor of patients with metastases. Furthermore, by using the LASSO regression model, we identified 23 DMRs that contributed to a high probability of liver metastasis of CRC. The leave-one-out cross validation (LOOCV) was used to evaluate model predictive performance at an AUC of 0.701. In particular, 7 out of those 23 DMRs were found to be in the promoter region of genes that were previously reported prognostic biomarkers in diverse tumor types, including TNNI2, PAX8, GUF1, KLF4, EVI2B, CEP112, and long non-coding RNA AC011298. In addition, the model was also able to distinguish metastases of different sites (liver or lung) at an AUC of 0.933. CONCLUSION: We have identified DNA methylation biomarkers associated with the risk of cancer liver metastasis in early-stage CRC patients. A risk prediction model based on those epigenetic markers was proposed for outcome assessment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-021-01108-3.
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spelling pubmed-81908692021-06-10 Identification of DNA methylation biomarkers for risk of liver metastasis in early-stage colorectal cancer Li, Weihua Guo, Lei Tang, Wanxiangfu Ma, Yutong Wang, Xiaonan Shao, Yang Zhao, Hong Ying, Jianming Clin Epigenetics Research BACKGROUND: Liver metastases can occur even in CRC patients who underwent curative surgery. While evidence suggested that adjuvant chemotherapy can help to reduce the occurrence of liver metastases for certain patients, it is not a recommended routine as the side effects outweigh the potential benefits, especially in Stage II CRC patients. This study aims to construct a model for predicting liver metastasis risk using differential methylation signals in primary CRC tumors, which can facilitate the decision for adjuvant chemotherapy. METHODS: Fifty-nine stage I/II and IV CRC patients were enrolled. Primary tumor, adjacent normal tissue, and metastatic tumor tissues were subject to targeted bisulfite sequencing for DNA methylation. The Least Absolute Shrinkage and Selection Operator (LASSO) algorithm was used to identify potential DMRs for predicting liver metastasis of CRC. RESULTS: We identified a total of 241,573 DMRs by comparing the DNA methylation profile of primary tumors of stage II patients who developed metastasis to those who were metastasis-free during the follow up period. 213 DMRs were associated with poor prognosis, among which 182 DMRS were found to be hypermethylated in the primary tumor of patients with metastases. Furthermore, by using the LASSO regression model, we identified 23 DMRs that contributed to a high probability of liver metastasis of CRC. The leave-one-out cross validation (LOOCV) was used to evaluate model predictive performance at an AUC of 0.701. In particular, 7 out of those 23 DMRs were found to be in the promoter region of genes that were previously reported prognostic biomarkers in diverse tumor types, including TNNI2, PAX8, GUF1, KLF4, EVI2B, CEP112, and long non-coding RNA AC011298. In addition, the model was also able to distinguish metastases of different sites (liver or lung) at an AUC of 0.933. CONCLUSION: We have identified DNA methylation biomarkers associated with the risk of cancer liver metastasis in early-stage CRC patients. A risk prediction model based on those epigenetic markers was proposed for outcome assessment. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13148-021-01108-3. BioMed Central 2021-06-09 /pmc/articles/PMC8190869/ /pubmed/34108011 http://dx.doi.org/10.1186/s13148-021-01108-3 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
Li, Weihua
Guo, Lei
Tang, Wanxiangfu
Ma, Yutong
Wang, Xiaonan
Shao, Yang
Zhao, Hong
Ying, Jianming
Identification of DNA methylation biomarkers for risk of liver metastasis in early-stage colorectal cancer
title Identification of DNA methylation biomarkers for risk of liver metastasis in early-stage colorectal cancer
title_full Identification of DNA methylation biomarkers for risk of liver metastasis in early-stage colorectal cancer
title_fullStr Identification of DNA methylation biomarkers for risk of liver metastasis in early-stage colorectal cancer
title_full_unstemmed Identification of DNA methylation biomarkers for risk of liver metastasis in early-stage colorectal cancer
title_short Identification of DNA methylation biomarkers for risk of liver metastasis in early-stage colorectal cancer
title_sort identification of dna methylation biomarkers for risk of liver metastasis in early-stage colorectal cancer
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8190869/
https://www.ncbi.nlm.nih.gov/pubmed/34108011
http://dx.doi.org/10.1186/s13148-021-01108-3
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