Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-5486
Authors: Sprang, Maximilian
Paret, Claudia
Faber, Jörg
Title: CpG-islands as markers for liquid biopsies of cancer patients
Online publication date: 11-Dec-2020
Language: english
Abstract: The analysis of tumours using biomarkers in blood is transforming cancer diagnosis and therapy. Cancers are characterised by evolving genetic alterations, making it difficult to develop reliable and broadly applicable DNA-based biomarkers for liquid biopsy. In contrast to the variability in gene mutations, the methylation pattern remains generally constant during carcinogenesis. Thus, methylation more than mutation analysis may be exploited to recognise tumour features in the blood of patients. In this work, we investigated the possibility of using global CpG (CpG means a CG motif in the context of methylation. The p represents the phosphate. This is used to distinguish CG sites meant for methylation from other CG motifs or from mentions of CG content) island methylation profiles as a basis for the prediction of cancer state of patients utilising liquid biopsy samples. We retrieved existing GEO methylation datasets on hepatocellular carcinoma (HCC) and cell-free DNA (cfDNA) from HCC patients and healthy donors, as well as healthy whole blood and purified peripheral blood mononuclear cell (PBMC) samples, and used a random forest classifier as a predictor. Additionally, we tested three different feature selection techniques in combination. When using cfDNA samples together with solid tumour samples and healthy blood samples of different origin, we could achieve an average accuracy of 0.98 in a 10-fold cross-validation. In this setting, all the feature selection methods we tested in this work showed promising results. We could also show that it is possible to use solid tumour samples and purified PBMCs as a training set and correctly predict a cfDNA sample as cancerous or healthy. In contrast to the complete set of samples, the feature selections led to varying results of the respective random forests. ANOVA feature selection worked well with this training set, and the selected features allowed the random forest to predict all cfDNA samples correctly. Feature selection based on mutual information could also lead to better than random results, but LASSO feature selection would not lead to a confident prediction. Our results show the relevance of CpG islands as tumour markers in blood. Keywords: liquid biopsy; CpG islands; HCC
DDC: 500 Naturwissenschaften
500 Natural sciences and mathematics
570 Biowissenschaften
570 Life sciences
610 Medizin
610 Medical sciences
Institution: Johannes Gutenberg-Universität Mainz
Department: FB 04 Medizin
Place: Mainz
DOI: http://doi.org/10.25358/openscience-5486
Version: Published version
Publication type: Zeitschriftenaufsatz
Document type specification: Scientific article
License: CC BY
Information on rights of use: https://creativecommons.org/licenses/by/4.0/
Journal: Cells
9
8
Pages or article number: 1820
Publisher: MDPI
Publisher place: Basel
Issue date: 2020
ISSN: 2073-4409
Publisher URL: https://doi.org/10.3390/cells9081820
Publisher DOI: 10.3390/cells9081820
Appears in collections:JGU-Publikationen

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