Gutenberg Open Science: CpG-islands as markers for liquid biopsies of cancer patients

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-5486

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DC Field	Value	Language
dc.contributor.author	Sprang, Maximilian	-
dc.contributor.author	Paret, Claudia	-
dc.contributor.author	Faber, Jörg	-
dc.date.accessioned	2020-12-11T10:47:09Z	-
dc.date.available	2020-12-11T10:47:09Z	-
dc.date.issued	2020	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/5490	-
dc.description.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	en_GB
dc.description.sponsorship	DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin Mainz	de
dc.language.iso	eng	de
dc.rights	CC BY	-
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	-
dc.subject.ddc	500 Naturwissenschaften	de_DE
dc.subject.ddc	500 Natural sciences and mathematics	en_GB
dc.subject.ddc	570 Biowissenschaften	de_DE
dc.subject.ddc	570 Life sciences	en_GB
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	CpG-islands as markers for liquid biopsies of cancer patients	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-5486	-
jgu.type.contenttype	Scientific article	de
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Cells	de
jgu.journal.volume	9	de
jgu.journal.issue	8	de
jgu.pages.alternative	1820	de
jgu.publisher.year	2020	-
jgu.publisher.name	MDPI	de
jgu.publisher.place	Basel	de
jgu.publisher.uri	https://doi.org/10.3390/cells9081820	de
jgu.publisher.issn	2073-4409	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	500	de
jgu.subject.ddccode	570	de
jgu.subject.ddccode	610	de
jgu.publisher.doi	10.3390/cells9081820
jgu.organisation.ror	https://ror.org/023b0x485
Appears in collections:	JGU-Publikationen

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