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

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Abstract

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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Most cited references 31

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Trimmomatic: a flexible trimmer for Illumina sequence data

Anthony M. Bolger, Marc Lohse, Bjoern Usadel (2014)

Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.

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clusterProfiler: an R package for comparing biological themes among gene clusters.

Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)

Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.

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Cancer statistics, 2020

Rebecca Siegel, Kimberly D Miller, Ahmedin Jemal (2020)

Each year, the American Cancer Society estimates the numbers of new cancer cases and deaths that will occur in the United States and compiles the most recent data on population-based cancer occurrence. Incidence data (through 2016) were collected by the Surveillance, Epidemiology, and End Results Program; the National Program of Cancer Registries; and the North American Association of Central Cancer Registries. Mortality data (through 2017) were collected by the National Center for Health Statistics. In 2020, 1,806,590 new cancer cases and 606,520 cancer deaths are projected to occur in the United States. The cancer death rate rose until 1991, then fell continuously through 2017, resulting in an overall decline of 29% that translates into an estimated 2.9 million fewer cancer deaths than would have occurred if peak rates had persisted. This progress is driven by long-term declines in death rates for the 4 leading cancers (lung, colorectal, breast, prostate); however, over the past decade (2008-2017), reductions slowed for female breast and colorectal cancers, and halted for prostate cancer. In contrast, declines accelerated for lung cancer, from 3% annually during 2008 through 2013 to 5% during 2013 through 2017 in men and from 2% to almost 4% in women, spurring the largest ever single-year drop in overall cancer mortality of 2.2% from 2016 to 2017. Yet lung cancer still caused more deaths in 2017 than breast, prostate, colorectal, and brain cancers combined. Recent mortality declines were also dramatic for melanoma of the skin in the wake of US Food and Drug Administration approval of new therapies for metastatic disease, escalating to 7% annually during 2013 through 2017 from 1% during 2006 through 2010 in men and women aged 50 to 64 years and from 2% to 3% in those aged 20 to 49 years; annual declines of 5% to 6% in individuals aged 65 years and older are particularly striking because rates in this age group were increasing prior to 2013. It is also notable that long-term rapid increases in liver cancer mortality have attenuated in women and stabilized in men. In summary, slowing momentum for some cancers amenable to early detection is juxtaposed with notable gains for other common cancers.

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Author and article information

Contributors

Hong Zhao: zhaohong@cicams.ac.cn

Jianming Ying:

ORCID: http://orcid.org/0000-0002-7301-4118

jmying@cicams.ac.cn

Journal

Journal ID (nlm-ta): Clin Epigenetics

Journal ID (iso-abbrev): Clin Epigenetics

Title: Clinical Epigenetics

Publisher: BioMed Central (London )

ISSN (Print): 1868-7075

ISSN (Electronic): 1868-7083

Publication date (Electronic): 9 June 2021

Publication date PMC-release: 9 June 2021

Publication date Collection: 2021

Volume: 13

Electronic Location Identifier: 126

Affiliations

[1 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Department of Pathology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; No. 17 Panjiayuan Nanli, Beijing, 100021 China

[2 ]Nanjing Geneseeq Technology Inc., Nanjing, Jiangsu China

[3 ]GRID grid.506261.6, ISNI 0000 0001 0706 7839, Department of Hepatobiliary Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, , Chinese Academy of Medical Sciences and Peking Union Medical College, ; Beijing, 100021 China

Author information

Jianming Ying http://orcid.org/0000-0002-7301-4118

Article

Publisher ID: 1108

DOI: 10.1186/s13148-021-01108-3

PMC ID: 8190869

PubMed ID: 34108011

SO-VID: c4631f63-0118-4655-996f-1a2c76354304

License:

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/. The Creative Commons Public Domain Dedication waiver ( http://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.

History

Date received : 9 February 2021

Date accepted : 31 May 2021

Funding

Funded by: FundRef http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;

Award ID: 81702436

Funded by: Non-profit Central Research Institute Fund of Chinese Academy of Medical Sciences

Award ID: 2019PT310026

Funded by: CAMS Innovation Fund for Medical Sciences

Award ID: 2017-I2M-1-006

Funded by: National Key Research and Development Program

Award ID: 2017YFC1311005

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: colorectal cancer,liver metastasis,dmrs,dna methylation,biomarker

Data availability:

ScienceOpen disciplines: Genetics

Keywords: colorectal cancer, liver metastasis, dmrs, dna methylation, biomarker

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

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Abstract

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Supplementary Information

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Most cited references 31

Trimmomatic: a flexible trimmer for Illumina sequence data

clusterProfiler: an R package for comparing biological themes among gene clusters.

Cancer statistics, 2020

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Cited by 6

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