Dataset Information

Identification of differentially methylated genes in the malignant transformation of ovarian endometriosis.

ABSTRACT:

Background

Key roles for epigenetic mechanisms in tumorigenesis are well accepted, while the relationship between gene methylation and malignant transformation of ovarian endometriosis (EMS) was seldom reported. In this study, we aimed to screen for aberrantly methylated genes associated with the malignant transformation of ovarian EMS and to preliminarily verify the reliability of screened results by detecting the methylation status and protein expression of the candidate gene in a larger scale of formaldehyde-fixed and paraffin-embedded (FFPE) samples.

Methods

Methylated CpG island amplification coupled with representational difference analysis (MCA-RDA) was performed on 3 couples of endometriosis-associated ovarian carcinoma (EAOC) fresh samples to identify differentially methylated candidate genes related to malignant transformation of ovarian EMS; Methylation-specific PCR (MSP) and immunohistochemistry were performed in 30 EAOC samples to detected the methylation status and protein expression of RASSF2 gene to verify the reliability of MCA-RDA results.

Results

Nine differentially methylated genes were obtained by MCA-RDA as candidate genes for malignant transformation of EMS; Methylation frequency of RASSF2 in the neoplastic tissues of EAOC group was higher than that in the ectopic endometria (p < 0.05). While protein expression of RASSF2 in the neoplastic tissues was lower than that in the ectopic endometria of the EAOC group (p < 0.05) Absence of protein expression of RASSF2 was significantly correlated with the promoter methylation of the gene (p < 0.05).

Conclusions

RASSF2, RUNX3, GSTZ1, CYP2A, GBGT1, NDUFS1, SPOCK2, ADAM22, and TRIM36 were candidate genes for malignant transformation of ovarian EMS and epigenetic inactivation of RASSF2 by promoter hypermethylation is an early event in malignant transformation of ovarian EMS. The screen results were reliable and worthy of further study.

SUBMITTER: Ren F

PROVIDER: S-EPMC4105232 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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Publications

Identification of differentially methylated genes in the malignant transformation of ovarian endometriosis.

Ren Fang F Wang Dan-Bo DB Li Tong T Chen Ying-Han YH Li Yan Y

Journal of ovarian research 20140710

<h4>Background</h4>Key roles for epigenetic mechanisms in tumorigenesis are well accepted, while the relationship between gene methylation and malignant transformation of ovarian endometriosis (EMS) was seldom reported. In this study, we aimed to screen for aberrantly methylated genes associated with the malignant transformation of ovarian EMS and to preliminarily verify the reliability of screened results by detecting the methylation status and protein expression of the candidate gene in a larg ...[more]

PMID: 25298284

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Project description:Differentially methylated genes (DMGs) serve a crucial role in the pathogenesis of glioma via the regulation of the cell cycle, proliferation, apoptosis, migration, infiltration, DNA repair and signaling pathways. This study aimed to identify aberrant DMGs and pathways by comprehensive bioinformatics analysis. The gene expression profile of GSE28094 was downloaded from the Gene Expression Omnibus (GEO) database, and the GEO2R online tool was used to find DMGs. Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis of the DMGs were performed by using the Database for Annotation Visualization and Integrated Discovery. A protein-protein interaction (PPI) network was constructed with Search Tool for the Retrieval of Interacting Genes. Analysis of modules in the PPI networks was performed by Molecular Complex Detection in Cytoscape software, and four modules were performed. The hub genes with a high degree of connectivity were verified by The Cancer Genome Atlas database. A total of 349 DMGs, including 167 hypermethylation genes, were enriched in biological processes of negative and positive regulation of cell proliferation and positive regulation of transcription from RNA polymerase II promoter. Pathway analysis enrichment revealed that cancer regulated the pluripotency of stem cells and the PI3K-AKT signaling pathway, whereas 182 hypomethylated genes were enriched in biological processes of immune response, cellular response to lipopolysaccharide and peptidyl-tyrosine phosphorylation. Pathway enrichment analysis revealed cytokine-cytokine receptor interaction, type I diabetes mellitus and TNF signaling pathway. A total of 20 hub genes were identified, of which eight genes were associated with survival, including notch receptor 1 (NOTCH1), SRC proto-oncogene (also known as non-receptor tyrosine kinase, SRC), interleukin 6 (IL6), matrix metallopeptidase 9 (MMP9), interleukin 10 (IL10), caspase 3 (CASP3), erb-b2 receptor tyrosine kinase 2 (ERBB2) and epidermal growth factor (EGF). Therefore, bioinformatics analysis identified a series of core DMGs and pathways in glioma. The results of the present study may facilitate the assessment of the tumorigenicity and progression of glioma. Furthermore, the significant DMGs may provide potential methylation-based biomarkers for the precise diagnosis and targeted treatment of glioma.

Project description:AimEndometriosis is one of the most common reproductive system diseases, but the mechanisms of disease progression are still unclear. Due to its high recurrence rate, searching for potential therapeutic biomarkers involved in the pathogenesis of endometriosis is an urgent issue.MethodsDue to the similarities between endometriosis and ovarian cancer, four endometriosis datasets and one ovarian cancer dataset were downloaded from Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, followed by gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interaction (PPI) analyses. Then, we validated gene expression and performed survival analysis with ovarian serous cystadenocarcinoma (OV) datasets in TCGA/GTEx database, and searched for potential drugs in the Drug-Gene Interaction Database. Finally, we explored the miRNAs of key genes to find biomarkers associated with the recurrence of endometriosis.ResultsIn total, 104 DEGs were identified in the endometriosis datasets, and the main enriched GO functions included cell adhesion, extracellular exosome and actin binding. Fifty DEGs were identified between endometriosis and ovarian cancer datasets including 11 consistently regulated genes, and nine DEGs with significant expression in TCGA/GTEx. Only IGHM had both significant expression and an association with survival, three module DEGs and two significantly expressed DEGs had drug associations, and 10 DEGs had druggability.ConclusionsITGA7, ITGBL1 and SORBS1 may help us understand the invasive nature of endometriosis, and IGHM might be related to recurrence; moreover, these genes all may be potential therapeutic targets.KEY MESSAGEThis manuscript used a bioinformatics approach to find target genes for the treatment of endometriosis.This manuscript used a new approach to find target genes by drawing on common characteristics between ovarian cancer and endometriosis.We screened relevant therapeutic agents for target genes in the drug database, and performed histological validation of target genes with both expression and survival analysis difference in cancer databases.

Dataset Information

Identification of differentially methylated genes in the malignant transformation of ovarian endometriosis.

Background

Methods

Results

Conclusions

Publications

Identification of differentially methylated genes in the malignant transformation of ovarian endometriosis.

Similar Datasets

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