Walking pathways with positive feedback loops reveal DNA methylation biomarkers of colorectal cancer

Autor: Leonid S. Leskov, Alexander E. Kel, Manel Esteller, Olga V. Kel-Margoulis, Ulyana A. Boyarskikh, Sebastian Moran, Philip Stegmaier, Daria Stelmashenko, Anna Martínez-Cardús, Nikita Mandrik, Jeannette Koschmann, Edgar Wingender, Andrey Sokolov, Fedor A. Kolpakov, Ivan S. Yevshin, Mathias Krull, Maxim L. Filipenko
Jazyk: angličtina
Rok vydání: 2019
Předmět:
Male
Signal transduction
Biochemistry
Epigenesis
Genetic
0302 clinical medicine
Structural Biology
lcsh:QH301-705.5
Epigenomics
Transcription factor binding sites
Regulation of gene expression
Feedback
Physiological
0303 health sciences
DNA methylation
Applied Mathematics
Middle Aged
3. Good health
Computer Science Applications
Gene Expression Regulation
Neoplastic
CpG site
Genetic algorithm
Prognostic biomarkers
030220 oncology & carcinogenesis
lcsh:R858-859.7
Female
DNA microarray
Colorectal Neoplasms
Computational biology
Biology
lcsh:Computer applications to medicine. Medical informatics
03 medical and health sciences
Multi-omics analysis
medicine
Biomarkers
Tumor
Humans
Circulating DNA
Epigenetics
Molecular Biology
030304 developmental biology
Neoplasm Staging
Binding Sites
Gene Expression Profiling
Research
Cancer
medicine.disease
Colorectal cancer
DNA binding site
lcsh:Biology (General)
CpG Islands
Prognostic biomarkers
Colorectal cancer
Multi-omics analysis
DNA methylation
Circulating DNA
Transcription factor binding sites
Signal transduction
Genetic algorithm
Transcription Factors
Zdroj: BMC Bioinformatics, Vol 20, Iss S4, Pp 1-20 (2019)
BMC Bioinformatics
ISSN: 1471-2105
Popis: Background The search for molecular biomarkers of early-onset colorectal cancer (CRC) is an important but still quite challenging and unsolved task. Detection of CpG methylation in human DNA obtained from blood or stool has been proposed as a promising approach to a noninvasive early diagnosis of CRC. Thousands of abnormally methylated CpG positions in CRC genomes are often located in non-coding parts of genes. Novel bioinformatic methods are thus urgently needed for multi-omics data analysis to reveal causative biomarkers with a potential driver role in early stages of cancer. Methods We have developed a method for finding potential causal relationships between epigenetic changes (DNA methylations) in gene regulatory regions that affect transcription factor binding sites (TFBS) and gene expression changes. This method also considers the topology of the involved signal transduction pathways and searches for positive feedback loops that may cause the carcinogenic aberrations in gene expression. We call this method “Walking pathways”, since it searches for potential rewiring mechanisms in cancer pathways due to dynamic changes in the DNA methylation status of important gene regulatory regions (“epigenomic walking”). Results In this paper, we analysed an extensive collection of full genome gene-expression data (RNA-seq) and DNA methylation data of genomic CpG islands (using Illumina methylation arrays) generated from a sample of tumor and normal gut epithelial tissues of 300 patients with colorectal cancer (at different stages of the disease) (data generated in the EU-supported SysCol project). Identification of potential epigenetic biomarkers of DNA methylation was performed using the fully automatic multi-omics analysis web service “My Genome Enhancer” (MGE) (my-genome-enhancer.com). MGE uses the database on gene regulation TRANSFAC®, the signal transduction pathways database TRANSPATH®, and software that employs AI (artificial intelligence) methods for the analysis of cancer-specific enhancers. Conclusions The identified biomarkers underwent experimental testing on an independent set of blood samples from patients with colorectal cancer. As a result, using advanced methods of statistics and machine learning, a minimum set of 6 biomarkers was selected, which together achieve the best cancer detection potential. The markers include hypermethylated positions in regulatory regions of the following genes: CALCA, ENO1, MYC, PDX1, TCF7, ZNF43. Electronic supplementary material The online version of this article (10.1186/s12859-019-2687-7) contains supplementary material, which is available to authorized users.
Databáze: OpenAIRE
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