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. 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”). 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. 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.

Objective: Mutations in TP53 lead to loss of function (LOF) or gain of function (GOF) of the corresponding protein p53 and produce a different effect on the tumor. Our goal was to determine the spectrum of somatic TP53 variants in BRCA1/2 associated high-grade serous ovarian cancer (HGSOC). Methods: The population under study comprised of HGSOCs with pathogenic variants in BRCA1 (n = 78) or BRCA2 (n = 21). Only chemo-naive and platinum-sensitive patients were included in this study. The case group of the IARC database (n = 1249) with HGSOC not stratified by BRCA status was used as a reference. A custom NGS panel was used for sequencing TP53 and mutational hot-spots of other genes, and p53 expression was evaluated by immunohistochemistry for 68 cases of HGSOCs. Results: Somatic TP53 variants (95) or inhibition of wild-type p53 expression (3) were observed in 98 cases. The sample with normal p53 had CDKNA1 variants. The frequency of truncating variants was significantly higher than in the reference cohort (30.3 vs. 21.0%, p = 0.01). Most of the samples (41/68) demonstrated low (or absent) expression of p53, and 17 samples overexpressed p53. LOH was typical for TP53 nonsense variants (14/15). In total, 68/95 samples were LOH positive and showed LOH in all tumorous cells, thus indicating the driver effect of TP53 mutations. Three specimens had KRAS, BAX, APC, and CTNNB1 subclones variants. Conclusion: High frequency of TP53 truncating variants, the low expression of mutant p53, and low incidence of oncogene mutations show potential GOF properties of p53 to be poorly represented in BRCA1/2 associated HGSOC.