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

2020 ◽  
Author(s):  
Yinchen Shen ◽  
Mo Li ◽  
Kun Liu ◽  
Xiaoyin Xu ◽  
Shaopin Zhu ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Differentially Expressed Genes ◽  
Age Related Macular Degeneration ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks ◽  
Age Related ◽  
Differentially Methylated Genes ◽  
Low Expression

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 by integrated bioinformatic analysis. Methods: Data of expression profiling GSE29801 and methylation profiling GSE102952 were obtained from the Gene Expression Omnibus database. We analyzed differentially methylated genes and differentially expressed genes in R software. Functional enrichment and protein–protein interaction (PPI) network analysis were performed using R package and Search Tool for the Retrieval of Interacting Genes online database. Hub genes were identified using Cytoscape. Results: 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. After overlapping, 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 Public Health Genomics and Precision Health Knowledge Base. KEGG pathway enrichment analysis indicated 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, coenzyme binding, etc. Hyper-LGs were enriched in mRNA transport, nuclear speck, DNA binding, etc. In PPI networks analysis, 23 nodes and 2 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 may improve the understanding of this disease.

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

2020 ◽  
Author(s):  
Yinchen Shen ◽  
Mo Li ◽  
Kun Liu ◽  
Xiaoyin Xu ◽  
Shaopin Zhu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Macular Degeneration ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Age Related Macular Degeneration ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Age Related ◽  
Low Expression

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.

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

2020 ◽  
Author(s):  
Yinchen Shen ◽  
Mo Li ◽  
Kun Liu ◽  
Xiaoyin Xu ◽  
Shaopin Zhu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Macular Degeneration ◽  
Differentially Expressed Genes ◽  
Bioinformatics Analysis ◽  
Age Related Macular Degeneration ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Age Related ◽  
Low Expression

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.

scholarly journals Comprehensive analysis of DNA methylation and gene expression profiles in cholangiocarcinoma

2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Cheng Zhang ◽  
Bingye Zhang ◽  
Di Meng ◽  
Chunlin Ge
Keyword(s):  
Gene Expression ◽  
Dna Methylation ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks ◽  
Differentially Methylated Genes ◽  
Tumorigenesis And Progression

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.

scholarly journals Exploring the Key Genes and Pathways in the Formation of Corneal Scar Using Bioinformatics Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Binfeng Liu ◽  
Ang Li ◽  
Hongbo Wang ◽  
Jialin Wang ◽  
Gongwei Zhai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differentially Expressed Genes ◽  
Protein Interaction ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Signal Pathways ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Ppi Networks ◽  
Corneal Scar

The Corneal wound healing results in the formation of opaque corneal scar. In fact, millions of people around the world suffer from corneal scars, leading to loss of vision. This study aimed to identify the key changes of gene expression in the formation of opaque corneal scar and provided potential biomarker candidates for clinical treatment and drug target discovery. We downloaded Gene expression dataset GSE6676 from NCBI-GEO, and analyzed the Differentially Expressed Genes (DEGs), Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analyses, and protein-protein interaction (PPI) network. A total of 1377 differentially expressed genes were identified and the result of Functional enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) identification and protein-protein interaction (PPI) networks were performed. In total, 7 hub genes IL6 (interleukin-6), MMP9 (matrix metallopeptidase 9), CXCL10 (C-X-C motif chemokine ligand 10), MAPK8 (mitogen-activated protein kinase 8), TLR4 (toll-like receptor 4), HGF (hepatocyte growth factor), EDN1 (endothelin 1) were selected. In conclusion, the DEGS, Hub genes and signal pathways identified in this study can help us understand the molecular mechanism of corneal scar formation and provide candidate targets for the diagnosis and treatment of corneal scar.

scholarly journals Identification of aberrantly methylated differentially expressed genes in age-related macular degeneration

Medicine ◽  
2019 ◽  
Vol 98 (14) ◽  
pp. e15083
Author(s):  
Zixuan Xu ◽  
Zhaohui Ruan ◽  
Xuetao Huang ◽  
Qiang Liu ◽  
Zhaozhi Li ◽  
...  
Keyword(s):  
Macular Degeneration ◽  
Differentially Expressed Genes ◽  
Age Related Macular Degeneration ◽  
Differentially Expressed ◽  
Age Related

scholarly journals Bioinformatics Analysis of Differentially Expressed Genes Involved in Irritable Bowel Syndrome With Diarrhea

2021 ◽  
Author(s):  
Yuan-Mei Lou ◽  
Yan-Zhi Ge ◽  
Wen Chen ◽  
Lin Su ◽  
Jia-Qi Zhang ◽  
...  
Keyword(s):  
Irritable Bowel Syndrome ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks ◽  
Black Module ◽  
Irritable Bowel

Abstract Purpose: Irritable bowel syndrome with diarrhea (IBS-D) is a common functional gastrointestinal disorder around the world. However, the molecular mechanisms of IBS-D are still not well understood. This study was designed to identify key biomarkers and immune infiltration in the rectal mucosa of IBS-D by bioinformatics analysis. Methods: The gene expression profiles of GSE36701 were downloaded from the GEO database. The differentially expressed genes (DEGs) were identified and functional enrichment and pathway analyses were performed. Using STRING and Cytoscape, protein-protein interaction (PPI) networks were constructed and core genes were identified. Subsequently, 22 immune cell types of IBS-D tissues were explored by the Cell type Identification by Estimating Relative Subsets of RNA Transcripts. Finally, the co-expression network of DEGs was estimated by the weigh gene co-expression network analysis method to identify IBS-D-related modules and deeply hub genes. Results: 224 up-regulated and 171 down-regulated genes in IBS-D patients: Our analysis indicated that several DEGs might play crucial roles in IBS-D, such as CDC20, UBE2C, AURKA, CDC26, CKS1B and PSMB3. Later, we found that immune infiltrating cells such as T cells CD4 memory resting, M2 macrophages are crucial in IBS-D progression. In the end, a total of 9 co-expression gene modules were calculated and the black module was found to have the highest correlation. 15 hub genes were identified both in DEGs and the black module. Conclusions: This study identified molecular mechanisms and a series of candidate genes as well as significant pathways from the bioinformatics network, which may provide a diagnostic method and therapeutic targets for IBS-D.

scholarly journals Genomic Expression Profiling of Colorectal Cancer in Silico

2021 ◽  
Author(s):  
Feifei Liu ◽  
Yu Wang ◽  
Wenxue Li ◽  
Diancheng Li ◽  
Yuwei Xin ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Survival Analysis ◽  
Mrna Expression ◽  
Differentially Expressed Genes ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Differentially Expressed ◽  
Analysis Tool ◽  
Hub Genes ◽  
Normal Tissues

Abstract Background: Colorectal cancer (CRC) is one of the most common malignancies of the digestive system; the progression and prognosis of which are affected by a complicated network of genes and pathways. The aim of this study was to identify potential hub genes associated with the progression and prognosis of colorectal cancer (CRC).Methods: We obtained gene expression profiles from GEO database to search differentially expressed genes (DEGs) between CRC tissues and normal tissue. Subsequently, we conducted a functional enrichment analysis, generated a protein–protein interaction (PPI) network to identify the hub genes, and analyzed the expression validation of the hub genes. Kaplan–Meier plotter survival analysis tool was performed to evaluate the prognostic value of hub genes expression in CRC patients.Results: A total of 370 samples, involving CRC and normal tissues were enrolled in this article. 283 differentially expressed genes (DEGs), including 62 upregulated genes and 221 downregulated genes between CRC and normal tissues were selected. We finally filtered out 6 hub genes, including INSL5, MTIM, GCG, SPP1, HSD11B2, and MAOB. In the database of TCGA-COAD, the mRNA expression of INSL5, MT1M, HSD11B2, MAOB in tumor is lower than that in normal; the mRNA expression of SPP1 in tumor is higher than that in normal. In the HPA database, the expression of INSL5, GCG, HSD11B2, MAOB in tumor is lower than that in normal tissues; the expression of SPP1 in the tumor is higher than that in normal tissues. Survival analysis revealed that INSL5, GCG, SPP1 and MT1M may serve as prognostic biomarkers in CRC. Conclusions: We screened out six hub genes to predict the occurrence and prognosis of patients with CRC using bioinformatics methods, which may provide new targets and ideas for diagnosis, prognosis and individualized treatment for CRC.

scholarly journals Screening the Hub Genes and Analyzing the Mechanisms in Discharged COVID-19 Patients Retesting Positive through Bioinformatics Analysis

2021 ◽  
Author(s):  
Ke-Ying Fang ◽  
Gui-Ning Liang ◽  
Zhuo-Qing Zhuang ◽  
Yong-Xin Fang ◽  
Yu-Qian Dong ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Social Order ◽  
Expression System ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Virus Reactivation ◽  
Hub Genes ◽  
Messenger Ribonucleic Acid ◽  
Ppi Networks ◽  
Significant Enrichment

Abstract Background: With the worldwide spread of COVID-19, people’s health and social order have been exposed to enormous risks. After encountering patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation.Methods: A separate microarray was acquired from the Integrated Gene Expression System (GEO), and its samples were divided into two groups: a “convalescent-RTP” group consisting of recovery and “retesting-positive” (RTP) patients (group CR) and a “health-RTP” group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the gene ontology (GO) and Kyoto pluripotent stem cells (KEGG) of the genes and genomes. Subsequently, the protein–protein interaction (PPI) networks of each group were established and the hub genes were discovered using the cytoHubba plug-in.Results: In this study, 20 differentially expressed genes were identified, and 6622 genes were identified in the group CR, consisting of 5003 up-regulated and 1619 down-regulated genes. Meanwhile, 7335 genes were screened in the group HR, including 4323 up-regulated and 3012 down-regulated ones. The GO and KEGG analysis of the two groups revealed significant enrichment of these differentially expressed genes in pathways associated with immune response and apoptosis. In the PPI network constructed, 10 hub genes in group CR were identified, including TP53BP1, SNRPD1, SNRPD2, SF3B1, SNRNP200, MRPS16, MRPS9, CALM1, PPP2R1A, YWHAZ. Similarly, TP53BP1, RPS15, EFTUD2, MRPL16, MRPL17, MRPS14, RPL35A, MRPL32, MRPS6, POLR2G were selected as hub genes.Conclusions: Using the messenger ribonucleic acid (mRNA) expression data from GSE166253, we explore the pathogenesis of retesting positive in COVID-19 from the immune mechanism and molecular level. We found TP53BP1, SNRPD1 and SNRPD2 as hub genes in RTP patients. Hence, their regulatory pathway is vital to the management and prognostic prediction of RTP patients, rendering the further study of these hub genes necessary.

A Comprehensive Data Analysis of Differentially Regulated Genes in Melanoma

2020 ◽  
Author(s):  
Yanjie Han ◽  
Xinxin Li ◽  
Jiliang Yan ◽  
Chunyan Ma ◽  
Xin Wang ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Growth Factor Receptor ◽  
Principal Component ◽  
Distant Metastases ◽  
The Cancer Genome Atlas ◽  
Differentially Expressed ◽  
Hub Genes ◽  
Ppi Networks

Abstract Background: Melanoma is the most deadly tumor in skin tumors and is prone to distant metastases. The incidence of melanoma has increased rapidly in the past few decades, and current trends indicate that this growth is continuing. This study was aimed to explore the molecular mechanisms of melanoma pathogenesis and discover underlying pathways and genes associated with melanoma.Methods: We used high-throughput expression data to study differential expression profiles of related genes in melanoma. The differentially expressed genes (DEGs) of melanoma in GSE15605, GSE46517, GSE7553 and the Cancer Genome Atlas (TCGA) datasets were analyzed. Differentially expressed genes (DEGs) were identified by paired t-test. Then the DEGs were performed cluster and principal component analyses and protein–protein interaction (PPI) network construction. After that, we analyzed the differential genes through bioinformatics and got hub genes. Finally, the expression of hub genes was confirmed in the TCGA databases and collected patient tissue samples.Results: Total 144 up-regulated DEGs and 16 down-regulated DEGs were identified. A total of 17 gene ontology analysis (GO) terms and 11 pathways were closely related to melanoma. Pathway of pathways in cancer was enriched in 8 DEGs, such as junction plakoglobin (JUP) and epidermal growth factor receptor (EGFR). In the PPI networks, 9 hub genes were obtained, such as loricrin (LOR), filaggrin (FLG), keratin 5 (KRT5), corneodesmosin (CDSN), desmoglein 1 (DSG1), desmoglein 3 (DSG3), keratin 1 (KRT1), involucrin (IVL) and EGFR. The pathway of pathways in cancer and its enriched DEGs may play important roles in the process of melanoma. The hub genes of DEGs may become promising melanoma candidate genes. Five key genes FLG, DSG1, DSG3, IVL and EGFR were identified in the TCGA database and melanoma tissues.Conclusions: The results suggested that FLG, DSG1, DSG3, IVL and EGFR might play important roles and potentially be valuable in the prognosis and treatment of melanoma.

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