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Integrated bioinformatics analysis of aberrantly-methylated differentially-expressed genes and pathways in age-related macular degeneration.

ABSTRACT: BACKGROUND:Age-related macular degeneration (AMD) represents the leading cause of visual impairment in the aging population. The goal of this study was to identify aberrantly-methylated, differentially-expressed genes (MDEGs) in AMD and explore the involved pathways via integrated bioinformatics analysis. METHODS:Data from expression profile GSE29801 and methylation profile GSE102952 were obtained from the Gene Expression Omnibus database. We analyzed differentially-methylated genes and differentially-expressed genes using R software. Functional enrichment and protein-protein interaction (PPI) network analysis were performed using the R package and Search Tool for the Retrieval of Interacting Genes online database. Hub genes were identified using Cytoscape. RESULTS:In total, 827 and 592 genes showed high and low expression, respectively, in GSE29801; 4117 hyper-methylated genes and 511 hypo-methylated genes were detected in GSE102952. Based on overlap, we categorized 153 genes as hyper-methylated, low-expression genes (Hyper-LGs) and 24 genes as hypo-methylated, high-expression genes (Hypo-HGs). Four Hyper-LGs (CKB, PPP3CA, TGFB2, SOCS2) overlapped with AMD risk genes in the Public Health Genomics and Precision Health Knowledge Base. KEGG pathway enrichment analysis indicated that Hypo-HGs were enriched in the calcium signaling pathway, whereas Hyper-LGs were enriched in sphingolipid metabolism. In GO analysis, Hypo-HGs were enriched in fibroblast migration, membrane raft, and coenzyme binding, among others. Hyper-LGs were enriched in mRNA transport, nuclear speck, and DNA binding, among others. In PPI network analysis, 23 nodes and two edges were established from Hypo-HGs, and 151 nodes and 73 edges were established from Hyper-LGs. Hub genes (DHX9, MAPT, PAX6) showed the greatest overlap. CONCLUSION:This study revealed potentially aberrantly MDEGs and pathways in AMD, which might improve the understanding of this disease.

SUBMITTER: Shen Y

PROVIDER: S-EPMC7092446 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Integrated bioinformatics analysis of aberrantly-methylated differentially-expressed genes and pathways in age-related macular degeneration.

Shen Yinchen Y Li Mo M Liu Kun K Xu Xiaoyin X Zhu Shaopin S Wang Ning N Guo Wenke W Zhao Qianqian Q Lu Ping P Yu Fudong F Xu Xun X

BMC ophthalmology 20200324 1

<h4>Background</h4>Age-related macular degeneration (AMD) represents the leading cause of visual impairment in the aging population. The goal of this study was to identify aberrantly-methylated, differentially-expressed genes (MDEGs) in AMD and explore the involved pathways via integrated bioinformatics analysis.<h4>Methods</h4>Data from expression profile GSE29801 and methylation profile GSE102952 were obtained from the Gene Expression Omnibus database. We analyzed differentially-methylated gen ...[more]

PMID: 32209064

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Project description:BackgroundMethylation plays a key role in the aetiology and pathogenesis of prostate cancer (PCa). This study aimed to identify aberrantly methylated differentially expressed genes (DEGs) and pathways in PCa and explore the underlying mechanisms of tumourigenesis.MethodsExpression profile (GSE29079) and methylation profile (GSE76938) datasets were obtained from the Gene Expression Omnibus (GEO). We used R 3.4.4 software to assess aberrantly methylated DEGs. The Cancer Genome Atlas (TCGA) RNA sequencing and Illumina HumanMethylation450 DNA methylation data were utilized to validate screened genes. Functional enrichment analysis of the screened genes was performed, and a protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Gens (STRING). The results were visualized in Cytoscape. After confirmation using TCGA, cBioPortal was used to examine alterations in genes of interest. Then, protein localization in PCa cells was observed using immunohistochemistry.ResultsOverall, 536 hypomethylated upregulated genes were identified that were enriched in biological processes such as negative regulation of transcription, osteoblast differentiation, intracellular signal transduction, and the Wnt signalling pathway. Pathway enrichment showed significant changes in factors involved in AMPK signalling, cancer, and adherens junction pathways. The hub oncogenes were AKT1, PRDM10, and FASN. Additionally, 322 hypermethylated downregulated genes were identified that demonstrated enrichment in biological processes including positive regulation of the MAPK cascade, muscle contraction, ageing, and signal transduction. Pathway analysis indicated enrichment in arrhythmogenic right ventricular cardiomyopathy (ARVC), focal adhesion, dilated cardiomyopathy, and PI3K-AKT signalling. The hub tumour suppressor gene was FLNA. Immunohistochemistry showed that AKT1, FASN, and FLNA were mainly expressed in PCa cell cytoplasm, while PRDM10 was mainly expressed in nuclei.ConclusionsOur results identify numerous novel genetic and epigenetic regulatory networks and offer molecular evidence crucial to understanding the pathogenesis of PCa. Aberrantly methylated hub genes, including AKT1, PRDM10, FASN, and FLNA, can be used as biomarkers for accurate PCa diagnosis and treatment. In conclusion, our study suggests that AKT1, PRDM10, and FASN may be tumour promoters and that FLNA may be a tumour suppressor in PCa. We hope these findings will draw more attention to these hub genes in future cancer studies.

Project description:Background: Methylation plays a significant role in the etiology and pathogenesis of hepatocellular carcinoma (HCC). The aim of the present study is to identify aberrantly methylated-diferentially expressed genes (DEGs) and dysregulated pathways associated with the development of HCC through integrated analysis of gene expression and methylation microarray. Method: Aberrantly methylated-DEGs were identified from gene expression microarrays (GSE62232, GSE74656) and gene methylation microarrays (GSE44909, GSE57958). Functional enrichment and pathway enrichment analyses were performed through the database of DAVID. Protein-protein interaction (PPI) network was established by STRING and visualized in Cytoscape. Subsequently, overall survival (OS) analysis of hub genes was performed by OncoLnc. Finally, we validated the expression level of CDCA5 by quantitative real-time PCR (qRT-PCR) and western blotting, and performed Immunohistochemical experiments utilizing a tissue microarray. Cell growth assay and flow cytometry were behaved to explore the function of CDCA5. Results: Aberrantly methylated-DEGs were enriched in biological process, molecular function, cellular component and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway. Among them, cell cycle was enriched most frequently, and some terms associated with cancer were enriched, such as p53 signaling pathway, pathways in cancers, PI3K-Akt signaling pathway and AMPK signaling pathway. After survival analysis and validation in TCGA database including methylation and gene expression status, 12 hub genes were identified. Furthermore, the expression level of new gene CDCA5 was validated in HCC cell lines and hepatic normal cell lines through qRT-PCR and western blotting. In additional, immunohistochemistry experiments revealed higher CDCA5 protein expression from HCC tumor tissues compared with paracancer tissues by tissue microarray. Finally, through loss of function, we demonstrated that CDCA5 promoted proliferation by regulating the cell cycle. Conclusions: In summary, the present study implied possible aberrantly methylated-differentially expressed genes and dysregulated pathways in HCC by bioinformatics analysis and experiments, which could be helpful in understanding the molecular mechanisms underlying the development and progression of HCC. Hub genes including CDC20, AURKB, BIRC5, RRM2, MCM2, PTTG1, CDKN2A, NEK2, CENPF, RACGAP1, GNA14 and especially the new gene CDCA5 may serve as biomarkers for diagnosis, treatment and prognosis of HCC.

Project description:BackgroundAge-related macular degeneration (AMD) is the leading cause of blindness for people over 50 years old worldwide. The purpose of this study was to identify differentially expressed and methylated genes (DEMGs) and construct a co-expression network for AMD.MethodsMicroarray expression (GSE29801 dataset) and DNA methylation (GSE102952 dataset) profiles were retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were analyzed between AMD retina tissues and normal retina tissues. A protein-protein interaction (PPI) network was constructed and hub genes were screened, followed by functional enrichment analysis. Then, weighted gene co-expression network analysis (WGCNA) was conducted. The ARPE-19 cells were maintained in a hypoxic state to construct an AMD cellular model. Enzyme-linked immunosorbent assay (ELISA) and the real-time qPCR (RT-qPCR) were performed for validation.ResultsAfter overlapping, 16 hypermethylated and down-regulated genes and 15 hypomethylated and up-regulated genes were identified for extramacular AMD. A total of 4 hub genes (LMNB2, EMD, HLA-A, and HLA-B) were screened for AMD in the extramacular retina. Furthermore, 13 hypermethylated and down-regulated genes and 31 hypomethylated and up-regulated genes were identified for macular AMD. Among them, 11 hub genes (HLA-A, HLA-B, HLA-DRB1, IFITM3, SAT1, MAOB, CHRDL1, FSTL1, HSPA1A, AR, and YAP1) were considered hub genes. The DEMGs were distinctly related with immune-related biological processes and pathways. A total of 16 co-expression modules were constructed, of which 2 significantly correlated with AMD. The genes in the 2 modules were involved in various crucial signaling pathways. The HIF1α and VEGF levels were significantly up-regulated in cell supernatant of hypoxia-induced ARPE-19 cells, indicating that the AMD cellular model was successfully established. Hub genes including CHRDL, FSTL1, and IFITM3 displayed significantly higher expression in hypoxia-induced ARPE-19 cells compared to normal cells. Greater up-regulation of CHRDL, FSTL1, and IFITM3 expression was found in hypoxia-induced ARPE-19 cells than in normal cells.ConclusionsThese findings offered several key DEMGs and pathways for AMD and constructed AMD-related co-expression modules, deepening understanding of the pathogenesis of AMD.

Project description:Increasing evidence has indicated that modulation of epigenetic mechanisms, especially methylation and long-non-coding RNA (lncRNA) regulation, plays a pivotal role in the process of atherosclerosis; however, few studies focused on revealing the epigenetic-related subgroups during atherosclerotic progression using unsupervised clustering analysis. Hence, we aimed to identify the epigenetics-related differentially expressed genes associated with atherosclerosis subtypes and characterize their clinical utility in atherosclerosis. Eighty samples with expression data (GSE40231) and 49 samples with methylation data (GSE46394) from a large artery plaque were downloaded from the GEO database, and aberrantly methylated-differentially expressed (AMDE) genes were identified based on the relationship between methylation and expression. Furthermore, we conducted weighted correlation network analysis (WGCNA) and co-expression analysis to identify the core AMDE genes strongly involved in atherosclerosis. K-means clustering was used to characterize two subtypes of atherosclerosis in GSE40231, and then 29 samples were recognized as validation dataset (GSE28829). In a blood sample cohort (GSE90074), chi-square test and logistic analysis were performed to explore the clinical implication of the K-means clusters. Furthermore, significance analysis of microarrays and prediction analysis of microarrays (PAM) were applied to identify the signature AMDE genes. Moreover, the classification performance of signature AMDE gene-based classifier from PAM was validated in another blood sample cohort (GSE34822). A total of 1,569 AMDE mRNAs and eight AMDE long non-coding RNAs (lncRNAs) were identified by differential analysis. Through the WGCNA and co-expression analysis, 32 AMDE mRNAs and seven AMDE lncRNAs were identified as the core genes involved in atherosclerosis development. Functional analysis revealed that AMDE genes were strongly related to inflammation and axon guidance. In the clinical analysis, the atherosclerotic subtypes were associated with the severity of coronary artery disease and risk of adverse events. Eight genes, including PARP15, SERGEF, PDGFD, MRPL45, UBR1, STAU1, WIZ, and LSM4, were selected as the signature AMDE genes that most significantly differentiated between atherosclerotic subtypes. Ultimately, the area under the curve of signature AMDE gene-based classifier for atherosclerotic subtypes was 0.858 and 0.812 in GSE90074 and GSE34822, respectively. This study identified the AMDE genes (lncRNAs and mRNAs) that could be implemented in clinical clustering to recognize high-risk atherosclerotic patients.

Dataset Information

Integrated bioinformatics analysis of aberrantly-methylated differentially-expressed genes and pathways in age-related macular degeneration.

Publications

Integrated bioinformatics analysis of aberrantly-methylated differentially-expressed genes and pathways in age-related macular degeneration.

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