Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma.
ABSTRACT: Background:The incidence of cholangiocarcinoma (CCA) has risen in recent years, and it has become a significant health burden worldwide. However, the mechanisms underlying tumorigenesis and progression of this disease remain largely unknown. An increasing number of studies have demonstrated crucial biological functions of epigenetic modifications, especially DNA methylation, in CCA. The present study aimed to identify and analyze methylation-regulated differentially expressed genes (MeDEGs) involved in CCA tumorigenesis and progression by bioinformatics analysis. Methods:The gene expression profiling dataset (GSE119336) and gene methylation profiling dataset (GSE38860) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially methylated genes (DMGs) were identified using the limma packages of R and GEO2R, respectively. The MeDEGs were obtained by overlapping the DEGs and DMGs. Functional enrichment analyses of these genes were then carried out. Protein-protein interaction (PPI) networks were constructed using STRING and visualized in Cytoscape to determine hub genes. Finally, the results were verified based on The Cancer Genome Atlas (TCGA) database. Results:We identified 98 hypermethylated, downregulated genes and 93 hypomethylated, upregulated genes after overlapping the DEGs and DMGs. These genes were mainly enriched in the biological processes of the cell cycle, nuclear division, xenobiotic metabolism, drug catabolism, and negative regulation of proteolysis. The top nine hub genes of the PPI network were F2, AHSG, RRM2, AURKB, CCNA2, TOP2A, BIRC5, PLK1, and ASPM. Moreover, the expression and methylation status of the hub genes were significantly altered in TCGA. Conclusions:Our study identified novel methylation-regulated differentially expressed genes (MeDEGs) and explored their related pathways and functions in CCA, which may provide novel insights into a further understanding of methylation-mediated regulatory mechanisms in CCA.
Project description:Background: Abnormal epigenetic alterations can contribute to the development of human malignancies. Identification of these alterations for early screening and prognosis of clear cell renal cell carcinoma (ccRCC) has been a highly sought-after goal. Bioinformatic analysis of DNA methylation data provides broad prospects for discovery of epigenetic biomarkers. However, there is short of exploration of methylation-driven genes of ccRCC. Methods: Gene expression data and DNA methylation data in metastatic ccRCC were sourced from the Gene Expression Omnibus (GEO) database. Differentially methylated genes (DMGs) at 5'-C-phosphate-G- 3' (CpG) sites and differentially expressed genes (DEGs) were screened and the overlapping genes in DMGs and DEGs were then subject to gene set enrichment analysis. Next, the weighted gene co-expression network analysis (WGCNA) was used to search hub DMGs associated with ccRCC. Cox regression and ROC analyses were performed to screen potential biomarkers and develop a prognostic model based on the screened hub genes. Results: Three hundred and fourteen overlapping DMGs were obtained from two independent GEO datasets. The turquoise module contained 79 hub DMGs, which represent the most significant module screened by WGCNA. Furthermore, a total of 12 hub genes (CETN3, DCAF7, GPX4, HNRNPA0, NUP54, SERPINB1, STARD5, TRIM52, C4orf3, C12orf51, and C17orf65) were identified in the TCGA database by multivariate Cox regression analyses. All the 12 genes were then used to generate the model for diagnosis and prognosis of ccRCC. ROC analysis showed that these genes exhibited good diagnostic efficiency for metastatic and non-metastatic ccRCC. Furthermore, the prognostic model with the 12 methylation-driven genes demonstrated a good prediction of 5-year survival rates for ccRCC patients. Conclusion: Integrative analysis of DNA methylation data identified 12 signature genes, which could be used as epigenetic biomarkers for prognosis of metastatic ccRCC. This prognostic model has a good prediction of 5-year survival for ccRCC patients.
Project description:Background:Prostate cancer (PCa) is a malignancy cause of cancer deaths and frequently diagnosed in male. This study aimed to identify tumor suppressor genes, hub genes and their pathways by combined bioinformatics analysis. Methods:A combined analysis method was used for two types of microarray datasets (DNA methylation and gene expression profiles) from the Gene Expression Omnibus (GEO). Differentially methylated genes (DMGs) were identified by the R package minfi and differentially expressed genes (DEGs) were screened out via the R package limma. A total of 4451 DMGs and 1509 DEGs, identified with nine overlaps between DMGs, DEGs and tumor suppressor genes, were screened for candidate tumor suppressor genes. All these nine candidate tumor suppressor genes were validated by TCGA (The Cancer Genome Atlas) database and Oncomine database. And then, the gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed by DAVID (Database for Annotation, Visualization and Integrated Discovery) database. Protein-protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. At last, Kaplan-Meier analysis was performed to validate these genes. Results:The candidate tumor suppressor genes were IKZF1, PPM1A, FBP1, SMCHD1, ALPL, CASP5, PYHIN1, DAPK1 and CASP8. By validation in TCGA database, PPM1A, DAPK1, FBP1, PYHIN1, ALPL and SMCHD1 were significant. The hub genes were FGFR1, FGF13 and CCND1. These hub genes were identified from the PPI network, and sub-networks revealed by these genes were involved in significant pathways. Conclusion:In summary, the study indicated that the combined analysis for identifying target genes with PCa by bioinformatics tools promote our understanding of the molecular mechanisms and underlying the development of PCa. And the hub genes might serve as molecular targets and diagnostic biomarkers for precise diagnosis and treatment of PCa.
Project description:In order to better understand the etiology of obese type 2 diabetes (T2D) at the molecular level, the present study investigated the gene expression and DNA methylation profiles associated with T2D via systemic analysis. Gene expression (GSE64998) and DNA methylation profiles (GSE65057) from liver tissues of healthy controls and obese patients with T2D were downloaded from the Gene Expression Omnibus database. Differentially?expressed genes (DEGs) and differentially?methylated genes (DMGs) were identified using the Limma package, and their overlapping genes were additionally determined. Enrichment analysis was performed using the BioCloud platform on the DEGs and the overlapping genes. Using Cytoscape software, protein?protein interaction (PPI), transcription factor target networks and microRNA (miRNA) target networks were then constructed in order to determine associated hub genes. In addition, a further GSE15653 dataset was utilized in order to validate the DEGs identified in the GSE64998 dataset analyses. A total of 251 DEGs, including 124 upregulated and 127 downregulated genes, were detected, and a total of 9,698 genes were demonstrated to be differentially methylated in obese patients with T2D compared with non?obese healthy controls. A total of 103 overlapping genes between the two datasets were revealed, including 47 upregulated genes and 56 downregulated genes. The identified overlapping genes were revealed to be strongly associated with fatty acid and glucose metabolic pathways, in addition to oxidation/reduction. The overlapping genes cyclin D1 (CCND1), PPARG coactivator ? (PPARGC1A), fatty acid synthase (FASN), glucokinase (GCK), steraroyl?coA desaturase (SCD) and tyrosine aminotransferase (TAT) had higher degrees in the PPI, transcription target networks and miRNA target networks. In addition, among the 251 DEGs, a total of 35 DEGs were validated to be being shared genes between the datasets, which included a number of key genes in the PPI network, including CCND1, FASN and TAT. Abnormal gene expression and DNA methylation patterns that were implicated in fatty acid and glucose metabolic pathways and oxidation/reduction reactions were detected in obese patients with T2D. Furthermore, the CCND1, PPARGC1A, FANS, GCK, SCD and TAT genes may serve a role in the development of obesity?associated T2D.
Project description:Aberrant methylated genes (DMGs) play an important role in the etiology and pathogenesis of esophageal squamous cell carcinoma (ESCC). In this study, we aimed to integrate three cohorts profile datasets to ascertain aberrant methylated-differentially expressed genes and pathways associated with ESCC by comprehensive bioinformatics analysis. We downloaded data of gene expression microarrays (GSE20347, GSE38129) and gene methylation microarrays (GSE52826) from the Gene Expression Omnibus (GEO) database. Aberrantly differentially expressed genes (DEGs) were obtained by GEO2R tool. The David database was then used to perform Gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome pathway enrichment analyses on selected genes. STRING and Cytoscape software were used to construct a protein-protein interaction (PPI) network, then the modules in the PPI networks were analyzed with MCODE and the hub genes chose from the PPI networks were verified by Oncomine and TCGA database. In total, 291 hypomethylation-high expression genes and 168 hypermethylation-low expression genes were identified at the screening step, and finally found six mostly changed hub genes including KIF14, CDK1, AURKA, LCN2, TGM1, and DSG1. Pathway analysis indicated that aberrantly methylated DEGs mainly associated with the P13K-AKT signaling, cAMP signaling and cell cycle process. After validation in multiple databases, most hub genes remained significant. Patients with high expression of AURKA were associated with shorter overall survival. To summarize, we have identified six feasible aberrant methylated-differentially expressed genes and pathways in ESCC by bioinformatics analysis, potentially providing valuable information for the molecular mechanisms of ESCC. Our data combined the analysis of gene expression profiling microarrays and gene methylation profiling microarrays, simultaneously, and in this way, it can shed a light for screening and diagnosis of ESCC in future.
Project description:Cholangiocarcinoma (CCA) is acknowledged as the second most commonly diagnosed primary liver tumor and is associated with a poor patient prognosis. The present study aimed to explore the biological functions, signaling pathways and potential prognostic biomarkers involved in CCA through transcriptomic analysis. Based on the transcriptomic dataset of CCA from The Cancer Genome Atlas (TCGA), differentially expressed protein?coding genes (DEGs) were identified. Biological function enrichment analysis, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, was applied. Through protein?protein interaction (PPI) network analysis, hub genes were identified and further verified using open?access datasets and qRT?PCR. Finally, a survival analysis was conducted. A total of 1,463 DEGs were distinguished, including 267 upregulated genes and 1,196 downregulated genes. For the GO analysis, the upregulated DEGs were enriched in 'cadherin binding in cell?cell adhesion', 'extracellular matrix (ECM) organization' and 'cell?cell adherens junctions'. Correspondingly, the downregulated DEGs were enriched in the 'oxidation?reduction process', 'extracellular exosomes' and 'blood microparticles'. In regards to the KEGG pathway analysis, the upregulated DEGs were enriched in 'ECM?receptor interactions', 'focal adhesions' and 'small cell lung cancer'. The downregulated DEGs were enriched in 'metabolic pathways', 'complement and coagulation cascades' and 'biosynthesis of antibiotics'. The PPI network suggested that CDK1 and another 20 genes were hub genes. Furthermore, survival analysis suggested that CDK1, MKI67, TOP2A and PRC1 were significantly associated with patient prognosis. These results enhance the current understanding of CCA development and provide new insight into distinguishing candidate biomarkers for predicting the prognosis of CCA.
Project description:Diffuse large B-cell lymphoma (DLBCL) is the most common hematologic malignancy, however, specific tumor-associated genes and signaling pathways are yet to be deciphered. Differentially expressed genes (DEGs) were computed based on gene expression profiles from GSE32018, GSE56315, and The Cancer Genome Atlas (TCGA) DLBC. Overlapping DEGs were then evaluated for gene ontology (GO), pathways enrichment, DNA methylation, protein-protein interaction (PPI) network analysis as well as survival analysis. Seventy-four up-regulated and 79 down-regulated DEGs were identified. From PPI network analysis, majority of the DEGs were involved in cell cycle, oocyte meiosis, and epithelial-to-mesenchymal transition (EMT) pathways. Six hub genes including CDC20, MELK, PBK, prostaglandin D2 synthase (PTGDS), PCNA, and CDK1 were selected using the Molecular Complex Detection (MCODE). CDC20 and PTGDS were able to predict overall survival (OS) in TCGA DLBC and in an additional independent cohort GSE31312. Furthermore, CDC20 DNA methylation negatively regulated CDC20 expression and was able to predict OS in DLBCL. A two-gene panel consisting of CDC20 and PTGDS had a better prognostic value compared with CDC20 or PTGDS alone in the TCGA cohort (P=0.026 and 0.039). Overall, the present study identified a set of novel genes and pathways that may play a significant role in the initiation and progression of DLBCL. In addition, CDC20 and PTGDS will provide useful guidance for therapeutic applications.
Project description:Background:Cholangiocarcinoma (CCA) is a subtype of highly malignant hepatic tumor, which has low 5-year survival rate and poor clinical outcome. Only a few patients can be detected early and accepted with the surgery. Most of CCA patients are diagnosed in advanced stage, and the treatments are limited. As for the inoperable, advanced CCA patients, chemotherapy is the main treatment, due to lacking molecular targets, therapeutic effect is limited. Materials and methods:To explore potential therapeutic targets for CCA, we analyzed three microarray datasets derived from the Gene Expression Omnibus (GEO) database. Then, we used GEO2R tools of NCBI to discover the differentially expressed genes (DEGs) from the CCA and normal liver tumor microarrays (TMA). Subsequently, we used the Database for Annotation, Visualization and Integrated Discovery (DAVID GO) to perform the Gene Ontology function (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Then, we carried out the Cytoscape software to search for the hub genes downregulated in CCA and identify the protein-protein interaction (PPI) of these genes. Besides, we used the GEPIA tool to evaluate the differential expressions of hub genes in CCA patients. Then, we also used MEXPRESS database to detect the promoter methylation levels of hub genes in CCA and normal tissue samples. In addition, we evaluated the expression of these genes in CCA lines and normal bile tract cells after 5-AZA (DNA methyltransferase inhibitor) treatment. Results:A total of 115 downregulated DEGs were identified. Among them, 10 hub genes with a high degree of connectivity were picked out. Among these 10 hub genes, F2, AHSG, ALDH8A1, SERPIND1 and AGXT showed higher DNA methylation levels of promoter in CCA compared with normal liver tissues. Therefore, these 5 genes may be the potential DNA methylation biomarkers and therapeutic targets in CCA.
Project description:Streptococcus agalactiae is an important pathogenic bacterium causing great economic loss in Nile tilapia (Oreochromis niloticus) culture. Resistant and susceptible groups sharing the same genome showed significantly different resistance to S. agalactiae in the genetically improved farmed tilapia strain of Nile tilapia. The resistance mechanism is unclear. We determined genome-wide DNA methylation profiles in spleen of resistant and susceptible O. niloticus at 5 h postinfection with S. agalactiae using whole-genome bisulfite sequencing. The methylation status was higher in the spleen samples from resistant fish than in the susceptible group. A total of 10,177 differentially methylated regions were identified in the two groups, including 3725 differentially methylated genes (DMGs) (3129 hyper-DMGs and 596 hypo-DMGs). The RNA sequencing showed 2374 differentially expressed genes (DEGs), including 1483 upregulated and 891 downregulated. Integrated analysis showed 337 overlapping DEGs and DMGs and 82 overlapping DEGs and differentially methylated region promoters. By integrating promoter DNA methylation with gene expression, we revealed four immune-related genes (Arnt2, Nhr38, Pcdh10, and Ccdc158) as key factors in epigenetic mechanisms contributing to pathogen resistance. Our study provided systematic methylome maps to explore the epigenetic mechanism and reveal the methylation loci of pathogen resistance and identified methylation-regulated genes that are potentially involved in defense against pathogens.
Project description:Asthma has been the most common chronic disease in children that places a major burden for affected people and their families.An integrated analysis of microarrays studies was performed to identify differentially expressed genes (DEGs) in childhood asthma compared with normal control. We also obtained the differentially methylated genes (DMGs) in childhood asthma according to GEO. The genes that were both differentially expressed and differentially methylated were identified. Functional annotation and protein-protein interaction network construction were performed to interpret biological functions of DEGs. We performed q-RT-PCR to verify the expression of selected DEGs.One DNA methylation and 3 gene expression datasets were obtained. Four hundred forty-one DEGs and 1209 DMGs in childhood asthma were identified. Among which, 16 genes were both differentially expressed and differentially methylated in childhood asthma. Natural killer cell mediated cytotoxicity pathway, Jak-STAT signaling pathway, and Wnt signaling pathway were 3 significantly enriched pathways in childhood asthma according to our KEGG enrichment analysis. The PPI network of top 20 up- and downregulated DEGs consisted of 822 nodes and 904 edges and 2 hub proteins (UBQLN4 and MID2) were identified. The expression of 8 DEGs (GZMB, FGFBP2, CLC, TBX21, ALOX15, IL12RB2, UBQLN4) was verified by qRT-PCR and only the expression of GZMB and FGFBP2 was inconsistent with our integrated analysis.Our finding was helpful to elucidate the underlying mechanism of childhood asthma and develop new potential diagnostic biomarker and provide clues for drug design.
Project description:BACKGROUND:Solid predominant lung adenocarcinomas (LUAD) have distinct histopathological and clinical characteristics compared with nonsolid subtypes. A comprehensive comparison of altered genes found in solid and nonsolid subtypes has not previously been performed. In this study, we analyzed differences in gene expression, genetic mutations, and DNA methylation to better understand the risk factors for these two subtypes of LUAD. METHODS:Differentially expressed genes (DEGs) and differentially mutated genes (DMGs) were analyzed from RNA-seq data downloaded from The Cancer Genome Atlas (TCGA) and Broad Institute database. To understand the functional significance of molecular changes, we examined the DEGs and DMGs with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis. RESULTS:A total of 184 patients in the TCGA cohort and 140 patients in the Broad Institute cohort were included in this study. We identified 75 DEGs, of which 15 were upregulated and 56 downregulated in the solid group relative to the nonsolid group. The DEGs were mainly involved in the regulation of water and fluid transport. We discovered 38 significantly differentially expressed genes that overlapped in the two groups. The DMGs were mainly enriched for pathways involved in cell-cell adhesion, cell adhesion, biological adhesion, and hemophilic cell adhesion. We additionally discovered nine significantly methylated genes between solid and nonsolid LUAD. CONCLUSIONS:Our study identified distinct DEGs, DMGs, and methylation genes for solid and nonsolid LUAD subtypes. These findings improve our understanding of the different carcinogenesis mechanisms in LUAD and will help to develop new therapeutic strategies.