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Identification of regulatory role of DNA methylation in colon cancer gene expression via systematic bioinformatics analysis.

ABSTRACT: Colon cancer arises from the accumulations of genetic and epigenetic changes. Currently, profiles of DNA methylation and gene expression of colon cancer have not been elucidated clearly. This articles aims to characterize the profile of DNA methylation and gene expression of colon cancer systemically, and acquire candidate genes potentially regulated by altered methylation for this disease.Data were downloaded from The Cancer Genome Atlas database. Differentially methylated CpG sites (DMCs) and differentially methylated regions (DMRs) were calculated via COHCAP. Differentially expressed genes (DEGs) were identified by DESeq2. Weighted gene co-expression network analysis (WGCNA) package in R was applied for WGCNA.Data of 275 solid tumor tissues and 19 adjacent tumor tissues of colon cancer were obtained. A total of 1828 DMCs, including 1390 hypermethylated and 438 hypomethylated CpG sites, were identified between tumor and normal groups. A total of 789 DEGs, containing 435 upregulated genes and 354 downregulated genes were observed. It revealed that 8 DMRs-DEGs and 95 DMCs-DEGs pairs were significantly correlated. Furthermore, genes of yellow and brown modules from WGCNA were significantly correlated with tumor/normal status, and significantly enriched in peroxisome proliferator activated receptor signaling pathway, glutamatergic synapse, and neuroactive ligand-receptor interaction. Genes in the above 2 modules were also significantly enriched in DMCs or DMRs-associated genes. Specifically, ADHFE1, HAND2, and GNAO1 were hypermethylated and downregulated in colon cancer, suggesting that the low expression levels of these genes may be regulated by DNA hypermethylation. In addition, the 3 genes were involved in brown module of WGCNA, indicating their important roles in colon cancer.The investigation of the relationship between DNA methylation and gene expression may help to understand the effect of DNA methylation alteration on genes expression, especially gene co-expression network in the development of colon cancer. Genes such as ADHFE1, HAND2, and GNAO1 may be served as potential candidates for diagnosis and therapy targets in colon cancer.

SUBMITTER: Yang Y

PROVIDER: S-EPMC5708922 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundDNA methylation, acknowledged as a key modification in the field of epigenetics, regulates gene expression at the transcriptional level. Aberrant methylation in DNA regulatory regions could upregulate oncogenes and downregulate tumor suppressor genes without changing the sequences. However, studies of methylation in the control of gene expression are still inadequate. In the present research, we performed bioinformatics analysis to clarify the function of methylation and supply candidate methylation-related biomarkers and drivers for colon cancer.AimTo identify and analyze methylation-regulated differentially expressed genes (MeDEGs) in colon cancer by bioinformatics analysis.MethodsWe downloaded RNA expression profiles, Illumina Human Methylation 450K BeadChip data, and clinical data of colon cancer from The Cancer Genome Atlas project. MeDEGs were identified by analyzing the gene expression and methylation levels using the edgeR and limma package in R software. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed in the DAVID database and KEGG Orthology-Based Annotation System 3.0, respectively. We then conducted Kaplan-Meier survival analysis to explore the relationship between methylation and expression and prognosis. Gene set enrichment analysis (GSEA) and investigation of protein-protein interactions (PPI) were performed to clarify the function of prognosis-related genes.ResultsA total of 5 up-regulated and 81 down-regulated genes were identified as MeDEGs. GO and KEGG pathway analyses indicated that MeDEGs were enriched in multiple cancer-related terms. Furthermore, Kaplan-Meier survival analysis showed that the prognosis was negatively associated with the methylation status of glial cell-derived neurotrophic factor (GDNF) and reelin (RELN). In PPI networks, GDNF and RELN interact with neural cell adhesion molecule 1. Besides, GDNF can interact with GDNF family receptor alpha (GFRA1), GFRA2, GFRA3, and RET. RELN can interact with RAFAH1B1, disabled homolog 1, very low-density lipoprotein receptor, lipoprotein receptor-related protein 8, and NMDA 2B. Based on GSEA, hypermethylation of GDNF and RELN were both significantly associated with pathways including "RNA degradation," "ribosome," "mismatch repair," "cell cycle" and "base excision repair."ConclusionAberrant DNA methylation plays an important role in colon cancer progression. MeDEGs that are associated with the overall survival of patients may be potential targets in tumor diagnosis and treatment.

Project description:BackgroundColorectal cancer is an important death-related disease in the worldwide. However, specific colon cancer tumor markers currently used for diagnosis and treatment are few. The purpose of this study is to screen the potential colon cancer markers by bioinformatics and verify the results with experiments.MethodsGene expression data were downloaded from two different databases: TCGA database and GEO datasets, which were then analyzed by two different methods (difference analysis and WGCNA method). Venn and PPI analysis obtained the potential core genes, which were then performed the GO enrichment and KEGG pathway analysis. Expressions levels of NKD1 in colon carcinoma tissues were further confirmed by immunohistochemical staining and western blot assays. Moreover, the function was measured by MTT, clone formation, and tumor transplantation experiments. Importantly, co-immunoprecipitation, immunofluorescence, and protein stability assays were further performed to explore the underlying mechanism of NKD1 promoting cell proliferation.ResultsNine potential core genes highly expressed in colon cancer samples were screened out by bioinformatics analysis. NKD1, one of the hub genes, highly expressed in the colon carcinoma tissues could enhance the proliferation of colon cancer cells. Mechanism research demonstrated that NKD1 was essential for the combination between Wnt signalosome (DVL) and β-catenin, and that NKD1 knockout remarkably decreased the β-catenin expression. Immunofluorescence assays further implied that NKD1 knockout significantly inhibited β-catenin nuclear accumulation. Importantly, the stability of β-catenin proteins was maintained by NKD1 in the colon cancer cells.ConclusionWe believe that NKD1 well expressed in the colorectal carcinoma tissues can enhance the proliferation of colon cancer cells. Furthermore, the functions that NKD1 may have in colon cancer cells should be different from that NKD1 has played in the zebrafish. Thus, NKD1 could be a specific colorectal cancer marker.

Project description:Osteoarthritis (OA) is a common type of arthritis, which may cause pain and disability. Alterations in gene expression and DNA methylation have been proven to be associated with the development of OA. The aim of the present study was to identify potential therapeutic targets and associated processes for OA via the combined analysis of gene expression and DNA methylation datasets. The gene expression and DNA methylation profiles were obtained from the Gene Expression Omnibus, and differentially expressed genes (DEGs) and differentially methylated sites (DMSs) were identified in the present study, using R programming software. The enriched functions of DEGs and DMSs were obtained via the Database for Annotation, Visualization and Integrated Discovery. Finally, cross analysis of DEGs and DMSs was performed to identify genes that exhibited differential expression and methylation simultaneously. The protein?protein interaction (PPI) network of overlaps between DEGs and DMSs was obtained using the Human Protein Reference Database; the topological properties of PPI network overlaps were additionally obtained. Hub genes in the PPI network were further confirmed via reverse transcription?quantitative polymerase chain reaction (RT?qPCR). The results of the present study revealed that the majority of DEGs and DMSs were upregulated and hypomethylated in patients with OA, respectively. DEGs and DMSs were primarily involved in inflammatory, immune and gene expression regulation?associated processes and pathways. Cross analysis revealed 30 genes that exhibited differential expression and methylation in OA simultaneously. Topological analysis of the PPI network revealed that numerous genes, including G protein subunit ?1 (GNAI1), runt related transcription factor 2 (RUNX2) and integrin subunit ?2 (ITGB2), may be involved in the development of OA. Additionally, RT?qPCR analysis of GNAI1, RUNX2 and ITGB2 provided further confirmation. Numerous known and novel therapeutic targets were obtained via network analysis. The results of the present study may be beneficial for the diagnosis and treatment of OA.

Project description:BACKGROUND:DNA methylation is a major epigenetic modification involved in regulating gene expression. The effects of DNA methylation on gene expression differ by genomic location and vary across kingdoms, species and environmental conditions. To identify the functional regulatory roles of DNA methylation, the correlation between DNA methylation changes and alterations in gene expression is crucial. With the advance of next-generation sequencing, genome-wide methylation and gene expression profiling have become feasible. Current bioinformatics tools for investigating such correlation are designed to the assessment of DNA methylation at CG sites. The correlation of non-CG methylation and gene expression is very limited. Some bioinformatics databases allow correlation analysis, but they are limited to specific genomes such as that of humans and do not allow user-provided data. RESULTS:Here, we developed a bioinformatics web tool, MethGET (Methylation and Gene Expression Teller), that is specialized to analyse the association between genome-wide DNA methylation and gene expression. MethGET is the first web tool to which users can supply their own data from any genome. It is also the tool that correlates gene expression with CG, CHG, and CHH methylation based on whole-genome bisulfite sequencing data. MethGET not only reveals the correlation within an individual sample (single-methylome) but also performs comparisons between two groups of samples (multiple-methylomes). For single-methylome analyses, MethGET provides Pearson correlations and ordinal associations to investigate the relationship between DNA methylation and gene expression. It also groups genes with different gene expression levels to view the methylation distribution at specific genomic regions. Multiple-methylome analyses include comparative analyses and heatmap representations between two groups. These functions enable the detailed investigation of the role of DNA methylation in gene regulation. Additionally, we applied MethGET to rice regeneration data and discovered that CHH methylation in the gene body region may play a role in the tissue culture process, which demonstrates the capability of MethGET for use in epigenomic research. CONCLUSIONS:MethGET is a Python software that correlates DNA methylation and gene expression. Its web interface is publicly available at https://paoyang.ipmb.sinica.edu.tw/Software.html. The stand-alone version and source codes are available on GitHub at https://github.com/Jason-Teng/MethGET.

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Identification of regulatory role of DNA methylation in colon cancer gene expression via systematic bioinformatics analysis.

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