scholarly journals P0998IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES AND SIGNALING PATHWAYS IN DIABETIC NEPHROPATHY BY BIOINFORMATICS ANALYSIS

2020 ◽  
Vol 35 (Supplement_3) ◽  
Author(s):  
Shumei Tang ◽  
Xiangcheng Xiao
Keyword(s):  
Diabetic Nephropathy ◽  
Differentially Expressed Genes ◽  
Complement Activation ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Differentially Expressed ◽  
Biological Processes ◽  
Ppi Network ◽  
String Database ◽  
Molecular Functions

Abstract Background and Aims Diabetes has considerable negative impact on morbidity and mortality and causes huge social and economic burden. As one of the most serious microvascular complication of diabetes, diabetic nephropathy (DN) leads to a large population of end-stage renal disease in many countries. The pathogenesis of DN is always a hot topic and the underlying molecular events are not completely clear. Tubular injury plays an important role and may be the initial event. Although few therapeutic treatments could postpone the onset and development, the morbidity of DN remains high. More available therapeutic treatments are urgently needed as well as early stage diagnostic markers and more credible prognostic molecular markers. As a wide range application of high-throughput omics technology, various public network database platforms have included extensive transcriptomics data for deeper bioinformatics analysis. Integrating these data provides better understandings of molecular functions and biological processes. We performed integrated bioinformatics to recognize differentially expressed genes and discussed potential molecular mechanisms in DN. Method The expression profiles of GSE30529, GSE47184, GSE99325 and GSE104954 were downloaded from the Gene Expression Omnibus database. The four microarray datasets were centralized, integrated and performed a difference analysis. Next, differentially expressed genes (DEGs) were deeply analyzed by gene ontology annotation and enrichment analysis. STRING database was used to conducted a PPI network and Molecular Complex Detection (MCODE) software was used to identify central genes. Results The four files contain 63 tubular biopsy samples from patients with DN and 41 control tubule samples. We identified 18 target DEGs, C3, PROM1, LUM, CPA3, SERPINA3, ANXA1, CX3CR1, AGR2, CD48, REG1A, RARRES1, CYP24A1, C1R, CFB, CDH6, PVALB, GADD45B and KLK1. GO analysis indicated that biological processes of DEGs concentrate on proteolysis, inflammatory response, complement activation and regulation of complement activation. Main cellular components include extracellular exosome, extracellular region, extracellular space, blood microparticle, protein complex and plasma membrane. Molecular functions include calcium ion binding and serine-type endopeptidase activity. DEGs were found that maybe mainly involved in staphylococcus aureus infection, renin-angiotensin system, and complement and coagulation cascades by KEGG pathway analysis. The PPI network of DEGs were established by STRING database and one significant modules were identified by MCODE software. In addition, 3 hub genes, C3, CX3CR1 and ANXA1, were discerned from the PPI network. Conclusion To better clarify the underlying molecular mechanisms and provide more effective targets, this study screened DEGs and pathways in DN using bioinformatics analyses.

scholarly journals Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

2020 ◽  
Author(s):  
Yanzhi Ge ◽  
Li Zhou ◽  
Zuxiang Chen ◽  
Yingying Mao ◽  
Ting Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Immune Infiltration ◽  
Significant Difference

Abstract Background: The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms. Materials and methods: The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA. Results: A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune. Conclusion: This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.

scholarly journals Identification of differentially expressed genes, signaling pathways and immune infiltration in rheumatoid arthritis by integrated bioinformatics analysis

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Yanzhi Ge ◽  
Li Zhou ◽  
Zuxiang Chen ◽  
Yingying Mao ◽  
Ting Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Immune Infiltration ◽  
Significant Difference

Abstract Background The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify differentially expressed genes (DEGs), pathways and immune infiltration involved in RA utilizing integrated bioinformatics analysis and investigating potential molecular mechanisms. Materials and methods The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 normal controls. The microarray datasets were consolidated and DEGs were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1) software, respectively. The protein-protein interaction (PPI) network of DEGs were developed utilizing the STRING database. Finally, the CIBERSORT was used to evaluate the infiltration of immune cells in RA. Results A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on cytokine receptor activity and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. The principal component analysis showed that there was a significant difference between the two tissues in infiltration immune. Conclusion This study shows that screening for DEGs, pathways and immune infiltration utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. Besides, our study provides valuable data related to DEGs, pathways and immune infiltration of RA and may provide new insight into the understanding of molecular mechanisms.

scholarly journals Identification and Validation of Potential Pathogenic Genes and Prognostic Markers in ESCC by Integrated Bioinformatics Analysis

2020 ◽  
Vol 11 ◽  
Author(s):  
Lu Tang ◽  
Yuqiao Chen ◽  
Xiong Peng ◽  
Yuan Zhou ◽  
Hong Jiang ◽  
...  
Keyword(s):  
Survival Analysis ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Prognostic Markers ◽  
Epithelial Mesenchymal Transition ◽  
Quantitative Polymerase Chain Reaction ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Migration Ability ◽  
Mesenchymal Transition

Esophageal squamous cell carcinoma (ESCC) is one of the most fatal malignancies of the digestive tract, but its underlying molecular mechanisms are not known. We aim to identify the genes involved in ESCC carcinogenesis and discover potential prognostic markers using integrated bioinformatics analysis. Three pairs of ESCC tissues and paired normal tissues were sequenced by high-throughput RNA sequencing (RNA-seq). Integrated bioinformatics analysis was used to identify differentially expressed coding genes (DECGs) and differentially expressed long non-coding RNA (lncRNA) genes (DELGs). A protein–protein interaction (PPI) network of DECGs was established using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website and visualized with Cytoscape. Survival analysis was conducted by log-rank tests to identify “hub” genes with potential prognostic value, and real-time reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was conducted to assess expression of these genes in ESCC tissues. TranswellTM assays were employed to examine the migration ability of cells after knockdown of LINC01614 expression, followed by investigation of epithelial–mesenchymal transition (EMT) by western blotting (WB). A total of 106 upregulated genes and 42 downregulated genes were screened out from the ESCC data sets. Survival analysis showed two hub protein-coding genes with higher expression in module 1 of the PPI network (SPP1 and BGN) and another three upregulated lncRNAs (LINC01614, LINC01415, NKILA) that were associated with a poor prognosis. High expression of SPP1, BGN, LINC01614, and LINC01415 in tumor samples was validated further by RT-qPCR. In vitro experiments show that knockdown of LINC01614 expression could significantly inhibit the migration of ESCC cells by regulating EMT, which was confirmed by WB. These results indicate that BGN, SPP1, LINC01614, and LINC01415 might be critical genes in ESCC and potential prognostic biomarkers.

scholarly journals Investigation of Aberrantly Methylated Differentially Expressed Genes in Pancreatic Cancer by Integrated Bioinformatics Analysis

2021 ◽  
Author(s):  
Cailin xue ◽  
Peng gao ◽  
Xudong zhang ◽  
Xiaohan cui ◽  
Lei jin ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Dna Sequences ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Biological Processes ◽  
Ppi Network ◽  
Hub Genes

Abstract Background: Abnormal methylation of DNA sequences plays an important role in the development and progression of pancreatic cancer (PC). The purpose of this study was to identify abnormal methylation genes and related signaling pathways in PC by comprehensive bioinformatic analysis of three datasets in the Gene Expression Omnibus (GEO). Methods: Datasets of gene expression microarrays (GSE91035, GSE15471) and gene methylation microarrays (GSE37480) were downloaded from the GEO database. Aberrantly methylated-differentially expressed genes (DEGs) were analysis by GEO2R software. GO and KEGG enrichment analyses of selected genes were performed using DAVID database. A protein–protein interaction (PPI) network was constructed by STRING and visualized in Cytoscape. Core module analysis was performed by Mcode in Cytoscape. Hub genes were obtained by CytoHubba app. in Cytoscape software. Results: A total of 267 hypomethylation-high expression genes, which were enriched in biological processes of cell adhesion, biological adhesion and regulation of signaling were obtained. KEGG pathway enrichment showed ECM-receptor interaction, Focal adhesion and PI3K-Akt signaling pathway. The top 5 hub genes of PPI network were EZH2, CCNA2, CDC20, KIF11, UBE2C. As for hypermethylation-low expression genes, 202 genes were identified, which were enriched in biological processes of cellular amino acid biosynthesis process and positive regulation of PI3K activity, etc. The pathways enriched were the pancreatic secretion and biosynthesis of amino acids pathways, etc. The five significant hub genes were DLG3, GPT2, PLCB1, CXCL12 and GNG7. In addition, five genes, including CCNA2, KIF11, UBE2C, PLCB1 and GNG7, significantly associated with patient's prognosis were also identified. Conclusion: Novel genes with abnormal expression were identified, which will help us further understand the molecular mechanism and related signaling pathways of PC, and these aberrant genes could possibly serve as biomarkers for precise diagnosis and treatment of PC.

scholarly journals Identification of differentially expressed genes and signaling pathways in rheumatoid arthritis by integrated bioinformatics analysis

2020 ◽  
Author(s):  
Yanzhi Ge ◽  
Li Zhou ◽  
Zuxiang Chen ◽  
Yingying Mao ◽  
Ting Li ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Receptor Interaction ◽  
Effective Prevention ◽  
Ppi Networks

Abstract Background The disability rate associated with rheumatoid arthritis (RA) ranks high among inflammatory joint diseases. However, the cause and potential molecular events are as yet not clear. Here, we aimed to identify key genes and pathways involved in RA utilizing integrated bioinformatics analysis and uncover underlying molecular mechanisms. Materials and methods The expression profiles of GSE55235, GSE55457, GSE55584 and GSE77298 were downloaded from the Gene Expression Omnibus database, which contained 76 synovial membrane samples, including 49 RA samples and 27 controls. The microarray datasets were consolidated and differentially expressed genes (DEGs) were acquired and further analyzed by bioinformatics techniques. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs were performed using R (version 3.6.1), respectively. The protein-protein interaction (PPI) networks of DEGs were developed utilizing the STRING database. Results A total of 828 DEGs were recognized, with 758 up-regulated and 70 down-regulated. GO and KEGG pathway analyses demonstrated that these DEGs focused primarily on multifactorial binding, transcription activity, cytokin-cytokin receptor interaction and relevant signaling pathways. The 30 most firmly related genes among DEGs were identified from the PPI network. Conclusion This study shows that screening for DEGs and pathways utilizing integrated bioinformatics analyses could aid in the comprehension of the molecular mechanisms involved in RA development. In addition, our study provides valuable data for the effective prevention, diagnosis, treatment and rehabilitation of RA patients as well as providing potential targets for the treatment of RA.

scholarly journals Identification of Aberrantly Expressed Genes during Aging in Rat Nucleus Pulposus Cells

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Shi Cheng ◽  
Xiaochuan Li ◽  
Linghan Lin ◽  
Zhiwei Jia ◽  
Yachao Zhao ◽  
...  
Keyword(s):  
Nucleus Pulposus ◽  
Differentially Expressed Genes ◽  
Molecular Mechanisms ◽  
Vital Role ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Nucleus Pulposus Cells ◽  
Hub Genes ◽  
Software Module ◽  
A Genome

Nucleus pulposus cells (NPCs) play a vital role in maintaining the homeostasis of the intervertebral disc (IVD). Previous studies have discovered that NPCs exhibited malfunction due to cellular senescence during disc aging and degeneration; this might be one of the key factors of IVD degeneration. Thus, we conducted this study in order to investigate the altered biofunction and the underlying genes and pathways of senescent NPCs. We isolated and identified NPCs from the tail discs of young (2 months) and old (24 months) SD rats and confirmed the senescent phenotype through SA-β-gal staining. CCK-8 assay, transwell assay, and cell scratch assay were adopted to detect the proliferous and migratory ability of two groups. Then, a rat Gene Chip Clariom™ S array was used to detect differentially expressed genes (DEGs). After rigorous bioinformatics analysis of the raw data, totally, 1038 differentially expressed genes with a fold change>1.5 were identified out of 23189 probes. Among them, 617 were upregulated and 421 were downregulated. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted and revealed numerous number of enriched GO terms and signaling pathways associated with senescence of NPCs. A protein-protein interaction (PPI) network of the DEGs was constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and Cytoscape software. Module analysis was conducted for the PPI network using the MCODE plugin in Cytoscape. Hub genes were identified by the CytoHubba plugin in Cytoscape. Derived 5 hub genes and most significantly up- or downregulated genes were further verified by real-time PCR. The present study investigated underlying mechanisms in the senescence of NPCs on a genome-wide scale. The illumination of molecular mechanisms of NPCs senescence may assist the development of novel biological methods to treat degenerative disc diseases.

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 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.

Screening and identification of differentially expressed genes between diabetic nephropathy glomerular and normal glomerular via bioinformatics technology

2020 ◽  
Vol 23 ◽  
Author(s):  
Junjie Du ◽  
Jihong Yang ◽  
Lingbing Meng
Keyword(s):  
Diabetic Nephropathy ◽  
Differentially Expressed Genes ◽  
Linear Regression Analysis ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Hub Genes ◽  
Glucose And Lipid Metabolism ◽  
Public Data ◽  
Multivariable Linear Regression Analysis

Background: Diabetes is a chronic metabolic disease characterized by disorders of glucose and lipid metabolism. Its most serious microvascular complication is diabetic nephropathy (DN), which is characterized by varying degrees of proteinuria and progressive glomerulosclerosis, eventually progressing to end-stage renal failure. Objective: The aim of this research is to identify hub genes which might serve as genetic markers to enhance the diagnosis, treatment, and prognosis of DN. Method: The procedures of the study include access to public data, identification of differentially expressed genes (DEGs) by GEO2R, and functional annotation of DEGs using enrichment analysis. Subsequently, construction of the protein-protein interaction (PPI) network and identification of significant modules were performed. Finally, the hub genes were identified and analyzed, including clustering analysis, Pearson's correlation coefficient analysis, and multivariable linear regression analysis. Results: Between the GSE30122 and GSE1009 datasets a total of 142 DEGs were identified, which were mainly enriched in cell migration, platelet activation, glomerulus development, glomerular basement membrane development, focal adhesion, regulation of actin cytoskeleton, and the PI3K-AKT signaling pathway. The PPI network was composed of 205 edges and 142 nodes. A total of 10 hub genes (VEGFA, NPHS1, WT1, PODXL, TJP1, FYN, SULF1, ITGA3, COL4A3, and FGF1) were identified from the PPI network. Conclusion: The DEGs between DN and control glomeruli samples may be involved in the occurrence and development of DN. We speculated that hub genes may be important inhibitory genes in the pathogenesis of diabetic nephropathy, so they are expected to become the new gene targets for the treatment of DN.

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