Differentially methylated and expressed genes in familial type 1 diabetes.
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ABSTRACT:
SUBMITTER: Dashti M
PROVIDER: S-EPMC9247163 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Background:Osteosarcoma (OS) is the most common primary bone tumors diagnosed in children and adolescents. Recent studies have shown a prognostic role of DNA methylation in various cancers, including OS. The aim of this study was to identify the aberrantly methylated genes that are prognostically relevant in OS. Methods:The differentially expressed mRNAs, miRNAs and methylated genes (DEGs, DEMs and DMGs respectively) were screened from various GEO databases, and the potential target genes of the DEMs were predicted by the RNA22 program. The protein-protein interaction (PPI) networks were constructed using the STRING database and visualized by Cytoscape software. The functional enrichment and survival analyses of the screened genes was performed using the R software. Results:Forty-seven downregulated hypermethylated genes and three upregulated hypomethylated genes were identified that were enriched in cell activation, migration and proliferation functions, and were involved in cancer-related pathways like JAK-STAT and PI3K-AKT. Eight downregulated hypermethylated tumor suppressor genes (TSGs) were identified among the screened genes based on the TSGene database. These hub genes are likely involved in OS genesis, progression and metastasis, and are potential prognostic biomarkers and therapeutic targets. Conclusions:TSGs including PYCARD, STAT5A, CXCL12 and CXCL14 were aberrantly methylated in OS, and are potential prognostic biomarkers and therapeutic targets. Our findings provide new insights into the role of methylation in OS progression.
| S-EPMC7186728 | biostudies-literature
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.
| S-EPMC7767999 | biostudies-literature
Project description:Background: We aimed to make an integrated analysis of published transcriptome and DNA methylation dataset to ascertain the key differentially methylated and differentially expressed genes for Alzherimer's disease (AD). Methods: Two gene expression microarrays and 1 gene methylation microarray were downloaded for identification of differentially expressed genes and differentially methylated genes. Then, we used various biological information databases to annotate the functions of the differentially-methylated/expressed genes, and screen out key genes and important signaling pathways. Finally, we validate the differentially-methylated/expressed genes in the additional online datasets and in blood from AD patients.Results: A total of 8 hub hypomethylated-high expression genes were obtained, including Rac family small GTPase 2, FGR proto-oncogene, Src family tyrosine kinase, LYN proto-oncogene, Src family tyrosine kinase, protein kinase C delta, myosin IF, integrin subunit alpha 5, semaphorin 4D, and growth arrest specific protein 7. Some enriched signaling pathways of hypomethylated-high expression genes were identified, including regulation of actin cytoskeleton, chemokine signaling pathway, Fc gamma R-mediated phagocytosis, and axon guidance. Conclusion: Differentially-methylated/expressed genes are likely to be associated with AD.
| S-EPMC10624077 | biostudies-literature
Project description:BackgroundMethylation plays an important role in the etiology and pathogenesis of colorectal cancer (CRC). This study aimed to identify aberrantly methylated-differentially expressed genes (DEGs) and pathways in CRC by comprehensive bioinformatics analysis.MethodsData of gene expression microarrays (GSE68468, GSE44076) and gene methylation microarrays (GSE29490, GSE17648) were downloaded from GEO database. Aberrantly methylated-DEGs were obtained by GEO2R. Functional and enrichment analyses of selected genes were performed using DAVID database. Protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. MCODE was used for module analysis of the PPI network.ResultsTotally 411 hypomethylation-high expression genes were identified, which were enriched in biological processes of response to wounding or inflammation, cell proliferation and adhesion. Pathway enrichment showed cytokine-cytokine receptor interaction, p53 signaling and cell cycle. The top 5 hub genes of PPI network were CAD, CCND1, ATM, RB1 and MET. Additionally, 239 hypermethylation-low expression genes were identified, which demonstrated enrichment in biological processes including cell-cell signaling, nerve impulse transmission, etc. Pathway analysis indicated enrichment in calcium signaling, maturity onset diabetes of the young, cell adhesion molecules, etc. The top 5 hub genes of PPI network were EGFR, ACTA1, SST, ESR1 and DNM2. After validation in TCGA database, most hub genes still remained significant.ConclusionIn summary, our study indicated possible aberrantly methylated-differentially expressed genes and pathways in CRC by bioinformatics analysis, which may provide novel insights for unraveling pathogenesis of CRC. Hub genes including CAD, CCND1, ATM, RB1, MET, EGFR, ACTA1, SST, ESR1 and DNM2 might serve as aberrantly methylation-based biomarkers for precise diagnosis and treatment of CRC in the future.
| S-EPMC5545832 | biostudies-literature
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.
| S-EPMC6360310 | biostudies-literature
Project description:BackgroundWe attempt to identify specific differentially methylated and expressed genes in people with longevity family history, it will contribute to discover significant features about human longevity.MethodsA prevalence study was conducted during October 2017 to January 2019 in Bama County of Guangxi, China and individuals were recruited and grouped into longevity family (n=60) and non-longevity family (n=60) to identify differentially methylated genes (DMGs). The expression profile dataset GSE16717 was downloaded from the GEO database in which individuals were divided into 3 groups, namely longevity (n=50), longevity offspring (n=50) and control (n=50) for identifying differentially expressed genes (DEGs). It was considered significantly different when P or adjusted P≤0.05.ResultsIn total, 117 longevity-related hypermethylated genes enriched in interleukin secretion/production regulation, chemokine signaling pathway and natural killer cell-mediated cytotoxicity. Another 296 significant key longevity-related DEGs primarily involved in protein binding, nucleus, cytoplasm, T cell receptor signaling pathway and Metabolic pathway, H19 and PFKFB4 were found to be both methylated and downregulated in people with longevity family history.ConclusionHuman longevity-specific genes involve in many immunity regulations and cellular immunity pathways, H19 and PFKFB4 show hypermethylated and suppressed status in people with longevity family history and might serve as longevity candidate genes.
| S-EPMC8213620 | biostudies-literature
Project description:ObjectiveTo identify key genes in hepatitis C virus (HCV)-induced cirrhosis and to predict effective drugs for its treatment.MethodsThree datasets were used to screen for differentially expressed genes (DEGs) and differentially methylated genes (DMGs) in HCV-induced cirrhosis. DEGs were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses using the clusterProfiler R package. Their respective protein-protein interaction (PPI) networks were constructed using Cytoscape. Cross analysis of DEGs and DMGs was performed to identify the genetic landscape of HCV-induced cirrhosis, and five genes were validated by receiver operating characteristic curve analysis. Molecular autodocking between ISG15 and natural products was performed using AutoDock Tool 1.5.6.ResultsA total of 357 DEGs and 8,830 DMGs were identified. DEG functional analysis identified several pathways involved in the pathogenesis of HCV-induced cirrhosis. Cross analysis of DEGs and DMGs identified 212 genes, and PPI network analysis identified 25 hub genes. Finally, five genes including ISG15 were identified and confirmed in dataset GSE36411. Artesunate and betulinic acid were shown to have a strong binding affinity to ISG15.ConclusionOur study provides novel insights into the mechanisms of HCV-induced cirrhosis which could lead to the identification of new therapeutics.
| S-EPMC8801647 | biostudies-literature
Project description:AIM:To discover methylated-differentially expressed genes (MDEGs) in hepatocellular carcinoma (HCC) and to explore relevant hub genes and potential pathways. METHODS:The data of expression profiling GSE25097 and methylation profiling GSE57956 were gained from GEO Datasets. We analyzed the differentially methylated genes and differentially expressed genes online using GEO2R. Functional and enrichment analyses of MDEGs were conducted using the DAVID database. A protein-protein interaction (PPI) network was performed by STRING and then visualized in Cytoscape. Hub genes were ranked by cytoHubba, and a module analysis of the PPI network was conducted by MCODE in Cytoscape software. RESULTS:In total, we categorized 266 genes as hypermethylated, lowly expressed genes (Hyper-LGs) referring to endogenous and hormone stimulus, cell surface receptor linked signal transduction and behavior. In addition, 161 genes were labelled as hypomethylated, highly expressed genes (Hypo-HGs) referring to DNA replication and metabolic process, cell cycle and division. Pathway analysis illustrated that Hyper-LGs were enriched in cancer, Wnt, and chemokine signalling pathways, while Hypo-HGs were related to cell cycle and steroid hormone biosynthesis pathways. Based on PPI networks, PTGS2, PIK3CD, CXCL1, ESR1, and MMP2 were identified as hub genes for Hyper-LGs, and CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10 were hub genes for Hypo-HGs by combining six ranked methods of cytoHubba. CONCLUSION:In the study, we disclose numerous novel genetic and epigenetic regulations and offer a vital molecular groundwork to understand the pathogenesis of HCC. Hub genes, including PTGS2, PIK3CD, CXCL1, ESR1, MMP2, CDC45, DTL, AURKB, CDKN3, MCM2, and MCM10, can be used as biomarkers based on aberrant methylation for the accurate diagnosis and treatment of HCC.
| S-EPMC6021769 | biostudies-literature
Project description:Introduction: Abnormal status of gene expression plays an important role in tumorigenesis, progression and metastasis of breast cancer. Mechanisms of gene silence or activation were varied. Methylation of genes may contribute to alteration of gene expression. This study aimed to identify differentially expressed hub genes which may be regulated by DNA methylation and evaluate their prognostic value in breast cancer by bioinformatic analysis. Methods: GEO2R was used to obtain expression microarray data from GSE54002, GSE65194 and methylation microarray data from GSE20713, GSE32393. Differentially expressed-aberrantly methylated genes were identified by FunRich. Biological function and pathway enrichment analysis were conducted by DAVID. PPI network was constructed by STRING and hub genes was sorted by Cytoscape. Expression and DNA methylation of hub genes was validated by UALCAN and MethHC. Clinical outcome analysis of hub genes was performed by Kaplan Meier-plotter database for breast cancer. IHC was performed to analyze protein levels of EXO1 and Kaplan-Meier was used for survival analysis. Results: 677 upregulated-hypomethylated and 361 downregulated-hypermethylated genes were obtained from GSE54002, GSE65194, GSE20713 and GSE32393 by GEO2R and FunRich. The most significant biological process, cellular component, molecular function enriched and pathway for upregulated-hypomethylated genes were viral process, cytoplasm, protein binding and cell cycle respectively. For downregulated-hypermethylated genes, the result was peptidyl-tyrosine phosphorylation, plasma membrane, transmembrane receptor protein tyrosine kinase activity and Rap1 signaling pathway (All p< 0.05). 12 hub genes (TOP2A, MAD2L1, FEN1, EPRS, EXO1, MCM4, PTTG1, RRM2, PSMD14, CDKN3, H2AFZ, CCNE2) were sorted from 677 upregulated-hypomethylated genes. 4 hub genes (EGFR, FGF2, BCL2, PIK3R1) were sorted from 361 downregulated-hypermethylated genes. Differential expression of 16 hub genes was validated in UALCAN database (p<0.05). 7 in 12 upregulated-hypomethylated and 2 in 4 downregulated-hypermethylated hub genes were confirmed to be significantly hypomethylated or hypermethylated in breast cancer using MethHC database (p<0.05). Finally, 12 upregulated hub genes (TOP2A, MAD2L1, FEN1, EPRS, EXO1, MCM4, PTTG1, RRM2, PSMD14, CDKN3, H2AFZ, CCNE2) and 3 downregulated genes (FGF2, BCL2, PIK3R1) contributed to significant unfavorable clinical outcome in breast cancer (p<0.05). High expression level of EXO1 protein was significantly associated with poor OS in breast cancer patients (p=0.03). Conclusion: Overexpression of TOP2A, MAD2L1, FEN1, EPRS, EXO1, MCM4, PTTG1, RRM2, PSMD14, CDKN3, H2AFZ, CCNE2 and downregulation of FGF2, BCL2, PIK3R1 might serve as diagnosis and poor prognosis biomarkers in breast cancer by more research validation. EXO1 was identified as an individual unfavorable prognostic factor. Methylation might be one of the major causes leading to abnormal expression of those genes. Functional analysis and pathway enrichment analysis of those genes would provide novel ideas for breast cancer research.
| S-EPMC6856906 | biostudies-literature
Project description:Prostate cancer (PCa) is the most frequent urological malignancy in men worldwide. DNA methylation has an essential role in the etiology and pathogenesis of PCa. The purpose of the present study was to identify the aberrantly methylated‑differentially expressed genes and to determine their potential roles in PCa. The important node genes identified were screened by integrated analysis. Gene expression microarrays and gene methylation microarrays were downloaded and aberrantly methylated‑differentially expressed genes were obtained. Enrichment analysis and protein‑protein interactions (PPI) were obtained, their interactive and visual networks were created, and the node genes in the PPI network were validated. A total of 105 hypomethylation‑high expression genes and 561 hypermethylation‑low expression genes along with their biological processes were identified. The top 10 node genes obtained from the PPI network were identified for each of the two gene groups. The methylation and gene expression status of node genes in TCGA database, GEPIA tool, and the HPA database were generally consistent with those of our results. In conclusion, the present study identified 20 aberrantly methylated‑differentially expressed genes in PCa by combining bioinformatics analyses of gene expression and gene methylation microarrays, and concurrently, the survival of these genes was analyzed. Notably, methylation is a reversible biological process, which makes it of great biological significance for the diagnosis and treatment of prostate cancer using bioinformatics technology to determine abnormal methylation gene markers. The present study provided novel therapeutic targets for the treatment of PCa.
| S-EPMC6947816 | biostudies-literature