Colorectal cancer (CRC) is one of the leading causes of death by cancer worldwide and in need of novel potential diagnostic biomarkers for early discovery. We conducted a two-step study. We first employed bioinformatics on data from The Cancer Genome Atlas to obtain potential biomarkers and then experimentally validated some of them on our clinical samples. Our aim was to find a methylation alteration common to all clusters, with the potential of becoming a diagnostic biomarker in CRC. Unsupervised clustering of methylation data resulted in four clusters, none of which had a known common genetic or epigenetic event, such as mutations or methylation. The intersect among clusters and regulatory regions resulted in 590 aberrantly methylated probes, belonging to 198 differentially expressed genes. After performing pathway and functional analysis on differentially expressed genes, we selected six genes: CEP55, FOXD3, FOXF2, GNAO1, GRIA4 and KCNA5, for further experimental validation on our own clinical samples. In silico analysis demonstrated that CEP55 was hypomethylated in 98.7% and up-regulated in 95.0% of samples. Genes FOXD3, FOXF2, GNAO1, GRIA4 and KCNA5 were hypermethylated in 97.9, 81.1, 80.3, 98.4 and 94.0%, and down-regulated in 98.3, 98.9, 98.1, 98.1 and 98.6% of samples, respectively. Our experimental data show CEP55 was hypomethylated in 97.3% of samples and down-regulated in all samples, while FOXD3, FOXF2, GNAO1, GRIA4 and KCNA5 were hypermethylated in 100.0, 90.2, 100.0, 99.1 and 100.0%, and down-regulated in 68.0, 76.0, 96.0, 95.2 and 84.0% of samples, respectively. Results of in silico and our experimental analyses showed that more than 97% of samples had at least four methylation markers altered. Using bioinformatics followed by experimental validation, we identified a set of six genes that were differentially expressed in CRC compared to normal mucosa and whose expression seems to be methylation dependent. Moreover, all of these six genes were common in all methylation clusters and mutation statuses of CRC and as such are believed to be an early event in human CRC carcinogenesis and to represent potential CRC biomarkers.

“miRNA colorectal cancer” (https://mirna-coadread.omics.si/) is a freely available web application for studying microRNA and mRNA expression and their correlation in colorectal cancer. To the best of our knowledge, “miRNA colorectal cancer” has the largest knowledge base of miRNA-target gene expressions and correlations in colorectal cancer, based on the largest available sample size from the same source of data. Data from high-throughput molecular profiling of 295 colon and rectum adenocarcinoma samples from The Cancer Genome Atlas was analyzed and integrated into our knowledge base. The objective of developing this web application was to help researchers to discover the behavior and role of miRNA-target gene interactions in colorectal cancer. For this purpose, results of differential expression and correlation analyses of miRNA and mRNA data collected in our knowledge base are available through web forms. To validate our knowledge base experimentally, we selected genesFN1,TGFB2,RND3,ZEB1andZEB2and miRNAshsa-miR-200a/b/c-3p,hsa-miR-141-3pandhsa-miR-429. Both approaches revealed a negative correlation between miRNAhsa-miR-200b/c-3pand its target geneFN1and betweenhsa-miR-200a-3pand its targetTGFB2, thus supporting the usefulness of the developed knowledge base.

Ulcerative colitis (UC) and Crohn’s disease (CD) are characterized by an imbalance between pro-inflammatory and anti-inflammatory cytokines, interfering with the resolution of inflammation. Due to the crucial role of cytokines, new insights into their profiles in UC and CD would help to improve our understanding of pathogenesis and enable the development of new treatment modalities. We provide an expression profile of cytokines in UC and CD, using bioinformatics approach, and experimental validation of expression of the selected genes. We retrieved data and analyzed the cytokine gene expression profiles of UC and CD. From ten genes with inverse expression, common to CD and UC, BMP8B, LEFTY1 and INSL5 were selected for gene expression experimental validation. Experimentally, BMP8B and INSL5 were down-regulated in both CD and UC but followed the bioinformatics trend. The expression of genes LEFTY1 and BMP8B was statistically significant when comparing UC and CD in colon and the expression of gene LEFTY1 showed statistical significance when CD in ileum and colon were compared. Using the bioinformatics approach and experimental validation, we found differences in expression profiles between UC and CD for INSL5, LEFTY1 and BMP8B. These three promising candidate genes need to be further explored at different levels, such as DNA methylation and protein expression, to provide more evidence on their potential diagnostic role in CD and UC.

The increasing availability of RNA sequencing data has opened up numerous opportunities to analyze various RNA interactions, including microRNA-target interactions (MTIs). In response to the necessity for a specialized tool to study MTIs in cancer and normal tissues, we developed AmiCa (https://amica.omics.si/), a web server designed for comprehensive analysis of mature microRNA (miRNA) and gene expression in 32 cancer types. Data from 9498 tumor samples and 626 normal samples from The Cancer Genome Atlas were obtained through the Genomic Data Commons and used to calculate differential expression and miRNA-target gene (MTI) correlations. AmiCa provides data on differential expression of miRNAs/genes for cancers for which normal tissue samples were available. In addition, the server calculates and presents correlations separately for tumor and normal samples for cancers for which normal samples are available. Furthermore, it enables the exploration of miRNA/gene expression in all cancer types with different miRNA/gene expression. In addition, AmiCa includes a ranking system for genes and miRNAs that can be used to identify those that are particularly highly expressed in certain cancers compared to other cancers, facilitating targeted and cancer-specific research. Finally, the functionality of AmiCa is illustrated by two case studies.

The present study investigated the distribution of genotypes within single nucleotide polymorphisms (SNPs) in genes, related to PVR pathogenesis across European subpopulations. Genotype distributions of 42 SNPs among 96 Slovenian healthy controls were investigated and compared to genotype frequencies in 503 European individuals (Ensembl database) and their subpopulations. Furthermore, a case-control status was simulated to evaluate effects of allele frequency changes on statistically significant results in gene-association studies investigating functional polymorphisms. In addition, 96 healthy controls were investigated within 4 SNPs: rs17561 (IL1A), rs2069763 (IL2), rs2229094 (LTA), and rs1800629 (TNF) in comparison to PVR patients. Significant differences (P<0.05) in distribution of genotypes among 96 Slovenian participants and a European population were found in 10 SNPs: rs3024498 (IL10), rs315952 (IL1RN), rs2256965 (LST1), rs2256974 (LST1), rs909253 (LTA), rs2857602 (LTA), rs3138045 (NFKB1A), rs3138056 (NFKB1A), rs7656613 (PDGFRA), and rs1891467 (TGFB2), which additionally showed significant differences in genotype distribution among European subpopulations. This analysis also showed statistically significant differences in genotype distributions between healthy controls and PVR patients in rs17561 of the IL1A gene (OR, 3.00; 95% CI, 0.77–11.75; P=0.036) and in rs1800629 of the TNF gene (OR, 0.48; 95% CI, 0.27–0.87; P=0.014). Furthermore, we have shown that a small change (0.02) in minor allele frequency (MAF) significantly affects the statistical p value in case-control studies. In conclusion, the study showed differences in genotype distributions in healthy populations across different European countries. Differences in distribution of genotypes may have had influenced failed replication results in previous PVR-related SNP-association studies.

Dairy cows with a lower nitrogen excretion intensity (N_exi) excrete less nitrogen, ammonia (NH₃) and nitrous oxide (N₂O), a highly potent greenhouse gas (GHG), per kg of milk produced and therefore represent a lower environmental impact while maintaining food security. To date, the genomics background of N_exi is unknown. Here we performed a genetic association study, overlap analysis and functional enrichment analysis for N_exi in 875 genotyped dairy cows with 2,147 lactations from 200 herds. We identified 1456 single nucleotide polymorphisms (SNPs) that significantly affect N_exi. We found 140 SNPs overlapping with 148 protein-coding genes. The MAN1A1 gene is a strong candidate gene for N_exi. Genotype CC of rs42786248, the most significantly associated SNP located in the MAN1A1 gene, had higher N_exi than genotypes AA (p < 0.001) and AC (p < 0.001). We identified 33 genes involved in biological processes related to nitrogen metabolism. Our results form the basis for further research on the genomics background of N_exi. The identified SNPs serve as potential targets for selective breeding programs, aimed at reducing N_exi and associated NH₃ and N₂O emissions in cattle production, thus contributing to more environmentally sustainable milk production.