Dataset Information

Five Hub Genes Can Be The Potential DNA Methylation Biomarkers For Cholangiocarcinoma Using Bioinformatics Analysis.

ABSTRACT: 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.

SUBMITTER: Chen D

PROVIDER: S-EPMC6793468 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Five Hub Genes Can Be The Potential DNA Methylation Biomarkers For Cholangiocarcinoma Using Bioinformatics Analysis.

Chen Diyu D Wu Hao H He Bin B Lu Yuejie Y Wu Wenxuan W Liu Hua H Feng Xiaode X Chen Jianzhong J Wu Jian J

OncoTargets and therapy 20191011

<h4>Background</h4>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.<h4>Materials and methods</h4>To explore ...[more]

PMID: 31632083

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Project description:BackgroundCervical cancer belongs to one of the most common female cancers; yet, the exact underlying mechanisms are still elusive. Recently, microarray and sequencing technologies have been widely used for screening biomarkers and molecular mechanism discovery in cancer studies. In this study, we aimed to analyse the microarray datasets using comprehensive bioinformatics tools and identified novel biomarkers associated with the prognosis of patients with cervical cancer.MethodsThe differentially expressed genes (DEGs) from Gene Expression Omnibus (GEO) datasets including GSE138080, GSE113942 and GSE63514 were analysed using GEO2R tool. The functional enrichment analysis was performed using g:Profiler tool. The protein-protein interaction (PPI) network construction and hub genes identification were performed using the STRING database and Cytoscape software, respectively. The hub genes were subjected to expression and survival analysis in the cervical cancer. The EdU incorporation and Cell Counting Kit-8 assays were performed to evaluate the effects of hub gene knockdown on the proliferation of cervical cancer cells.ResultsA total of 89 overlapping DEGs (63 up-regulated and 26 down-regulated genes) were identified in the microarray datasets. The functional enrichment analysis indicated that the overlapping DEGs were mainly associated with "DNA replication" and "cell cycle". Furthermore, the PPI network analysis revealed that the network contains 87 nodes and 309 edges. Sub-module analysis using the Molecular Complex Detection tool identified 21 hub genes from the PPI network. The expression levels of the 21 hub genes were all up-regulated in the cervical cancer tissues when compared to normal cervical tissues as analysed by GEPIA tool. The survival analysis showed that the low expression of cell division cycle 45 (CDC45), GINS complex subunit 2 (GINS2), minichromosome maintenance complex component 2 (MCM2) and proliferating cell nuclear antigen (PCNA) was significantly correlated with the shorter overall survival of patients with cervical cancer. Moreover, the protein expression levels of GINS2, MCM2 and PCNA, but not CDC45, were significantly up-regulated in the cervical cancer tissues when compared to normal cervical tissues. Finally, knockdown of MCM2 significantly suppressed the proliferation of HeLa and SiHa cells.ConclusionIn conclusion, we screened a total of 89 overlapping DEGs from the GEO datasets, and further analysis identified four hub genes (CDC45, GINS2, MCM2 and PCNA) that were likely associated with the prognosis of patients with cervical cancer. MCM2 knockdown repressed the cervical cancer cell proliferation. The current findings may provide novel insights into understanding the pathophysiology of cervical cancer and develop therapeutic targets for patients with cervical cancer.

Project description:Background: Patients with Varicose veins (VV) show no obvious symptoms in the early stages, and it is a common and frequent clinical condition. DNA methylation plays a key role in VV by regulating gene expression. However, the molecular mechanism underlying methylation regulation in VV remains unclear. Methods: The mRNA and methylation data of VV and normal samples were obtained from the Gene Expression Omnibus (GEO) database. Methylation-Regulated Genes (MRGs) between VV and normal samples were crossed with VV-associated genes (VVGs) obtained by weighted gene co-expression network analysis (WGCNA) to obtain VV-associated MRGs (VV-MRGs). Their ability to predict disease was assessed using receiver operating characteristic (ROC) curves. Biomarkers were then screened using a random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). Next, gene set enrichment analysis (GSEA) was performed to explore the functions of biomarkers. Furthermore, we also predicted their drug targets, and constructed a competing endogenous RNAs (ceRNA) network and a drug target network. Finally, we verified their mRNA expression using quantitative real-time polymerase chain reaction (qRT-PCR). Results: Total three VV-MRGs, namely Wnt1-inducible signaling pathway protein 2 (WISP2), Cysteine-rich intestinal protein 1 (CRIP1), and Odd-skipped related 1 (OSR1) were identified by VVGs and MRGs overlapping. The area under the curves (AUCs) of the ROC curves for these three VV-MRGs were greater than 0.8. RF was confirmed as the optimal diagnostic model, and WISP2, CRIP1, and OSR1 were regarded as biomarkers. GSEA showed that WISP2, CRIP1, and OSR1 were associated with oxidative phosphorylation, extracellular matrix (ECM), and respiratory system functions. Furthermore, we found that lncRNA MIR17HG can regulate OSR1 by binding to hsa-miR-21-5p and that PAX2 might treat VV by targeting OSR1. Finally, qRT-PCR results showed that the mRNA expression of the three genes was consistent with the results of the datasets. Conclusion: This study identified WISP2, CRIP1, and OSR1 as biomarkers of VV through comprehensive bioinformatics analysis, and preliminary explored the DNA methylation-related molecular mechanism in VV, which might be important for VV diagnosis and exploration of potential molecular mechanisms.

Project description:Background: Rheumatoid arthritis (RA) is a common autoimmune disease characterized by progressive, destructive polyarthritis. However, the cause and underlying molecular events of RA are not clear. Here, we applied integrated bioinformatics to identify tissue-specific expressed hub genes involved in RA and reveal potential targeted drugs. Methods: Three expression profiles of human microarray datasets involving fibroblast-like synoviocytes (FLS) were downloaded from the Gene Expression Omnibus (GEO) database, the differentially expressed mRNAs (DEGs), miRNAs (DEMs), and lncRNAs (DELs) between normal and RA synovial samples were screened using GEO2R tool. BioGPS was used to identified tissue-specific expressed genes. Functional and pathway enrichment analyses were performed for common DEGs using the DAVID database, and the protein-protein interaction (PPI) network of common DEGs was constructed to recognize hub genes by the STRING database. Based on receiver operating characteristic (ROC) curve, we further investigated the prognostic values of tissue-specific expressed hub genes in RA patients. Connectivity Map (CMap) was run to identify novel anti-RA potential drugs. The DEM-DEG pairs and ceRNA network containing key DEMs were established by Cytoscape. Results: We obtain a total of 418 DEGs, 23 DEMs and 49 DELs. 64 DEGs were verified as tissue-specific expressed genes, most derive from the hematologic/immune system (20/64, 31.25%). GO term and KEGG pathway enrichment analysis showed that DEGs focused primarily on immune-related biological process and NF-κB pathway. 10 hub genes were generated via using MCODE plugin. Among them, SPAG5, CUX2, and THEMIS2 were identified as tissue-specific expressed hub genes, these 3 tissue-specific expressed hub genes have superior diagnostic value in the RA samples compared with osteoarthritis (OA) samples. 5 compounds (troleandomycin, levodopa, trichostatin A, LY-294002, and levamisole) rank among the top five in connectivity score. In addition, 5 miRNAs were identified to be key DEMs, the lncRNA-miRNA-mRNA network with five key DEMs was formed. The networks containing tissue-specific expressed hub genes are as follows: ARAP1-AS2/miR-20b-3p/TRIM3, ARAP1-AS2/miR-30c-3p/FRZB. Conclusion: This study indicates that screening for identify tissue-specific expressed hub genes and ceRNA network in RA using integrated bioinformatics analyses could help us understand the mechanism of development of RA. Besides, SPAG5 and THEMIS2 might be candidate biomarkers for diagnosis of RA. LY-294002, trichostatin A, and troleandomycin may be potential drugs for RA.

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Project description:Chronic hypoxia can be observed in the heart under physiological or pathophysiological states, including embryonic development or cyanotic congenital heart disease. The aim of the present study was to examine gene expression profiles of chronically hypoxic myocardium and to explore the pathophysiological mechanisms by which the heart adapts to chronic hypoxia. Raw data from the next‑generation sequencing data set GSE36761 were downloaded from the Gene Expression Omnibus database. The data set comprised 30 specimens, including 8 healthy myocardia and 22 tetralogy of Fallot (TOF) congenital cardiac malformations; only 7 original data sets of healthy myocardia were obtained, and 5/22 TOFs were excluded. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of differentially expressed genes (DEGs) were performed. Furthermore, network analysis of DEGs using Cytoscape software based on protein‑protein interaction (PPI) data was also conducted. A total of 1,260 DEGs were selected, of which 926 DEGs were enriched in 83 GO biological process terms, including extracellular matrix organization, regeneration and monocyte chemotaxis. Furthermore, 406 DEGs were enriched in 13 KEGG pathways, including cytokine‑cytokine receptor interaction, focal adhesion and apoptosis. PPI network analysis indicated that six hub genes with correlated degree scores >25 among nodes were identified, including G protein subunit β4, C‑C motif chemokine receptor (CCR)1, CCR2, platelet factor 4, catenin β1 and Jun proto‑oncogene (JUN). Of these, JUN was enriched in GO terms of regeneration and neuron projection regeneration, and in KEGG pathways of focal adhesion, apoptosis and Chagas disease (American trypanosomiasis). The present bioinformatics analysis of these DEGs and hub genes may provide a molecular insight to the role of diverse genes in the pathophysiology of chronically hypoxic myocardium and in myocardial adaptation to chronic hypoxia.

Dataset Information

Five Hub Genes Can Be The Potential DNA Methylation Biomarkers For Cholangiocarcinoma Using Bioinformatics Analysis.

Publications

Five Hub Genes Can Be The Potential DNA Methylation Biomarkers For Cholangiocarcinoma Using Bioinformatics Analysis.

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