Bioinformatics Analysis of Differentially Expressed Genes and Protein-Protein Interaction Networks Associated with Functional Pathways in Ulcerative Colitis.
ABSTRACT: BACKGROUND This bioinformatics study aimed to identify differentially expressed genes (DEGs) and protein-protein interaction (PPI) networks associated with functional pathways in ulcerative colitis based on 3 Gene Expression Omnibus (GEO) datasets. MATERIAL AND METHODS The GSE87466, GSE75214, and GSE48958 MINiML formatted family files were downloaded from the GEO database. DEGs were identified from the 3 datasets, and volcano maps and heat maps were drawn after R language standardization and analysis, respectively. Venn diagram software was used to identify common DEGs. PPI analysis of common DEGs was performed using the Search Tool for the Retrieval of Interacting Genes. Gene modules and hub genes were visualized in the PPI network using Cytoscape. Enrichment analysis was performed for all common DEGs, module genes, and hub genes. RESULTS A total of 90 DEGs were selected, which included 3 functional modules and 1 hub gene module. CXCL8 module genes were mainly enriched in cytokine-mediated signaling pathways and interleukin (IL)-10 signaling. CCL20 module genes were mainly enriched in the IL-17 signaling pathway and cellular response to IL-1. Hub gene modules mainly involved IL-10, IL-4, and IL-13 signaling pathways. CXCL8, CXCL1, and IL-1ß were the top 3 hub genes and were mainly involved in IL-10 signaling. CONCLUSIONS Bioinformatics analysis using 3 GEO datasets identified CXCL8, CXCL1, and IL-1ß, which are involved in IL-10 signaling, as the top 3 hub genes in ulcerative colitis. The findings from this study remain to be validated, but they may contribute to the further understanding of the pathogenesis of ulcerative colitis.
Project description:BACKGROUND Alzheimer disease (AD) is a common and fatal subtype of dementia that remains a challenge to diagnose and treat. This study aimed to identify potential biomarkers that influence the prognosis of AD. MATERIAL AND METHODS A total of 6 gene expression profiles from the Gene Expression Omnibus (GEO) database were assessed for their potential as AD biomarkers. We identified differentially expressed genes (DEGs) using the prediction analysis for microarray (PAM) algorithm and obtained hub genes through the analysis of the protein-protein interaction (PPI) network and module analysis. RESULTS We identified 6 gene expression profiles from the GEO database and assessed their potential as AD biomarkers. Shared gene sets were extracted and integrated into large expression profile matrices. We identified 2514 DEGs including 68 upregulated- and 2446 downregulated genes through analysis of the limma package. We screened 379 significant DEGs including 68 upregulated and 307 downregulated genes for their ability to distinguish AD from control samples using PAM algorithm. Functional enrichment of the 379 target genes was produced from Database for Annotation, Visualization and Integrated Discovery.(DAVID) and included histone function, beta receptor signaling, cell growth, and angiogenesis. The downregulated genes were significantly enriched in MAPK signaling, synaptic signaling, neuronal apoptosis and AD associated pathways. Upon analysis of the PPI network, 32 hub genes including ENO2, CCT2, CALM2, ACACB, ATP5B, MDH1, and PP2CA were screened. Of these hub genes, NFKBIA and ACACB were upregulated and 29 genes were downregulated in AD patients. CONCLUSIONS We screened 379 significant DEGs as potential biomarkers of AD using PAM and obtained 32 hub genes through PPI network and module analysis. These findings reveal new potential AD biomarkers with prognostic and therapeutic value.
Project description:Background:Understanding hub genes involved in gastric cancer (GC) metastasis could lead to effective approaches to diagnose and treat cancer. In this study, we aim to identify the hub genes and investigate the underlying molecular mechanisms of GC. Methods:To explore potential therapeutic targets for GC,three expression profiles (GSE54129, GSE33651, GSE81948) of the genes were extracted from the Gene Expression Omnibus (GEO) database. The GEO2R online tool was applied to screen out differentially expressed genes (DEGs) between GC and normal gastric samples. Database for Annotation, Visualization and Integrated Discovery was applied to perform Gene Ontology (GO) and KEGG pathway enrichment analysis. The protein-protein interaction (PPI) network of these DEGs was constructed using a STRING online software. The hub genes were identified by the CytoHubba plugin of Cytoscape software. Then, the prognostic value of these identified genes was verified by gastric cancer database derived from Kaplan-Meier plotter platform. Results:A total of 85 overlapped upregulated genes and 44 downregulated genes were identified. The majority of the DEGs were enriched in extracellular matrix organization, endodermal cell differentiation, and endoderm formation. Moreover, five KEGG pathways were significantly enriched, including ECM-receptor interaction, amoebiasis, AGE-RAGE signaling pathway in diabetic complications, focal adhesion, protein digestion and absorption. By combining the results of PPI network and CytoHubba, a total of nine hub genes including COL1A1, THBS1, MMP2, CXCL8, FN1, TIMP1, SPARC, COL4A1, and ITGA5 were selected. The Kaplan-Meier plotter database confirmed that overexpression levels of these genes were associated with reduced overall survival, except for THBS1 and CXCL8. Conclusions:Our study suggests that COL1A1, MMP2, FN1, TIMP1, SPARC, COL4A1, and ITGA5 may be potential biomarkers and therapeutic targets for GC. Further study is needed to assess the effect of THBS1 and CXCL8 on GC.
Project description:Osteoarthritis (OA) is one of the most common diseases worldwide, but the pathogenic genes and pathways are largely unclear. The aim of this study was to screen and verify hub genes involved in OA and explore potential molecular mechanisms. The expression profiles of GSE12021 and GSE55235 were downloaded from the Gene Expression Omnibus (GEO) database, which contained 39 samples, including 20 osteoarthritis synovial membranes and 19 matched normal synovial membranes. The raw data were integrated to obtain differentially expressed genes (DEGs) and were deeply analyzed by bioinformatics methods. The Gene Ontology (GO) and pathway enrichment of DEGs were performed by DAVID and Kyoto Encyclopedia of Genes and Genomes (KEGG) online analyses, respectively. The protein-protein interaction (PPI) networks of the DEGs were constructed based on data from the STRING database. The top 10 hub genes VEGFA, IL6, JUN, IL1β, MYC, IL4, PTGS2, ATF3, EGR1, and DUSP1 were identified from the PPI network. Module analysis revealed that OA was associated with significant pathways including TNF signaling pathway, cytokine-cytokine receptor interaction, and osteoclast differentiation. The qRT-PCR result showed that the expression level of IL6, VEGFA, JUN, IL-1β, and ATF3 was significantly increased in OA samples (p < 0.05), and these candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of OA.
Project description:Background and Objective: Non-small cell lung cancer (NSCLC) accounts for 80-85% of all patients with lung cancer and 5-year relative overall survival (OS) rate is less than 20%, so that identifying novel diagnostic and prognostic biomarkers is urgently demanded. The present study attempted to identify potential key genes associated with the pathogenesis and prognosis of NSCLC. Methods: Four GEO datasets (GSE18842, GSE19804, GSE43458, and GSE62113) were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) between NSCLC samples and normal ones were analyzed using limma package, and RobustRankAggreg (RRA) package was used to conduct gene integration. Moreover, Search Tool for the Retrieval of Interacting Genes database (STRING), Cytoscape, and Molecular Complex Detection (MCODE) were utilized to establish protein-protein interaction (PPI) network of these DEGs. Furthermore, functional enrichment and pathway enrichment analyses for DEGs were performed by Funrich and OmicShare. While the expressions and prognostic values of top genes were carried out through Gene Expression Profiling Interactive Analysis (GEPIA) and Kaplan Meier-plotter (KM) online dataset. Results: A total of 249 DEGs (113 upregulated and 136 downregulated) were identified after gene integration. Moreover, the PPI network was established with 166 nodes and 1784 protein pairs. Topoisomerase II alpha (TOP2A), a top gene and hub node with higher node degrees in module 1, was significantly enriched in mitotic cell cycle pathway. In addition, Interleukin-6 (IL-6) was enriched in amb2 integrin signaling pathway. The mitotic cell cycle was the most significant pathway in module 1 with the highest P-value. Besides, five hub genes with high degree of connectivity were selected, including TOP2A, CCNB1, CCNA2, UBE2C, and KIF20A, and they were all correlated with worse OS in NSCLC. Conclusion: The results showed that TOP2A, CCNB1, CCNA2, UBE2C, KIF20A, and IL-6 may be potential key genes, while the mitotic cell cycle pathway may be a potential pathway contribute to progression in NSCLC. Further, it could be used as a new biomarker for diagnosis and to direct the synthesis medicine of NSCLC.
Project description:The current study aimed to elucidate the molecular mechanisms and identify the potential key genes and pathways for metastatic uveal melanoma (UM) using bioinformatics analysis.Gene expression microarray data from GSE39717 included 39 primary UM tissue samples and 2 metastatic UM tissue samples. Differentially expressed genes (DEGs) were generated using Gene Expression Omnibus 2R. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using the online Database for Annotation, Visualization and Integrated Discovery (DAVID) tool. The web-based STRING tool was adopted to construct a protein--protein interaction (PPI) network. The MCODE tool in Cytoscape was used to generate significant modules of the PPI network.A total of 213 DEGs were identified. GO and KEGG analyses revealed that the upregulated genes were mainly enriched in extracellular matrix organization and blood coagulation cascades, while the downregulated DEGs were mainly related to protein binding, negative regulation of ERK cascade, nucleus and chromatin modification, and lung and renal cell carcinoma. The most significant module was extracted from the PPI network. GO and KEGG enrichment analyses of the module revealed that the genes were mainly enriched in the extracellular region and space organization, blood coagulation process, and PI3K-Akt signaling pathway. Hub genes, including FN1, APOB, F2, SERPINC1, SERPINA1, APOA1, FGG, PROC, ITIH2, VCAN, TFPI, CXCL8, CDH2, and HP, were identified from DEGs. Survival analysis and hierarchical clustering results revealed that most of the hub genes were associated with prognosis and clinical progression.Results of this bioinformatics analysis may provide predictive biomarkers and potential candidate therapeutic targets for individuals with metastatic UM.
Project description:Colorectal cancer (CRC) is a life-threatening disease with a poor prognosis. Therefore, it is crucial to identify molecular prognostic biomarkers for CRC. The present study aimed to identify potential key genes that could be used to predict the prognosis of patients with CRC. Three CRC microarray datasets (GSE20916, GSE73360 and GSE44861) were downloaded from the Gene Expression Omnibus (GEO) database, and one dataset was obtained from The Cancer Genome Atlas (TCGA) database. The three GEO datasets were analyzed to detect differentially expressed genes (DEGs) using the BRB-ArrayTools software. Functional and pathway enrichment analyses of these DEGs were performed using the Database for Annotation, Visualization and Integrated Discovery tool. A protein-protein interaction (PPI) network of DEGs was constructed, hub genes were extracted, and modules of the PPI network were analyzed. To investigate the prognostic values of the hub genes in CRC, data from the CRC datasets of TCGA were used to perform the survival analyses based on the sample splitting method and Cox regression model. Correlation among the hub genes was evaluated using Spearman's correlation analysis. In the three GEO datasets, a total of 105 common DEGs were identified, including 51 down- and 54 up-regulated genes in CRC compared with normal colorectal tissues. A PPI network consisting of 100 DEGs and 551 edges was constructed, and 44 nodes were identified as hub genes. Among these 44 genes, the four hub genes TIMP metallopeptidase inhibitor 1 (TIMP1), solute carrier family 4 member 4 (SLC4A4), aldo-keto reductase family 1 member B10 (AKR1B10) and ATP binding cassette subfamily E member 1 (ABCE1) were associated with overall survival (OS) in patients with CRC. Three significant modules were extracted from the PPI network. The hub gene TIMP1 was present in Module 1, ABCE1 was involved in Module 2 and SLC4A4 was identified in Module 3. Univariate analysis revealed that TIMP1, SLC4A4, AKR1B10 and ABCE1 were associated with the OS of patients with CRC. Multivariate analysis demonstrated that SLC4A4 may be an independent prognostic factor associated with OS. Furthermore, the results from correlation analysis revealed that there was no correlation between TIMP1, SLC4A4 and ABCE1, whereas AKR1B10 was positively correlated with SLC4A4. In conclusion, the four key genes TIMP1, SLC4A4, AKR1B10 and ABCE1 associated with the OS of patients with CRC were identified by integrated bioinformatics analysis. These key genes may be used as prognostic biomarkers to predict the survival of patients with CRC, and may therefore represent novel therapeutic targets for CRC.
Project description:Aims:To explore molecular mechanisms that link peri-implantitis and type 2 diabetes mellitus (T2DM) by bioinformatic analysis of publicly available experimental transcriptomic data. Materials and methods:Gene expression data from peri-implantitis were downloaded from the Gene Expression Omnibus database, integrated and differentially expressed genes (DEGs) in peri-implantitis were identified. Next, experimentally validated and computationally predicted genes related to T2DM were downloaded from the DisGeNET database. Protein-protein interaction network (PPI) pairs of DEGs related to peri-implantitis and T2DM related genes were constructed, "hub" genes and overlapping DEG were determined. Functional enrichment analysis was used to identify significant shared biological processes and signaling pathways. The PPI networks were subjected to cluster and specific class analysis for identifying "leader" genes. Module network analysis of the merged PPI network identified common or cross-talk genes connecting the two networks. Results:A total of 92 DEGs overlapped between peri-implantitis and T2DM datasets. Three hub genes (IL-6, NFKB1, and PIK3CG) had the highest degree in PPI networks of both peri-implantitis and T2DM. Three leader genes (PSMD10, SOS1, WASF3), eight cross-talk genes (PSMD10, PSMD6, EIF2S1, GSTP1, DNAJC3, SEC61A1, MAPT, and NME1), and one signaling pathway (IL-17 signaling) emerged as peri-implantitis and T2DM linkage mechanisms. Conclusions:Exploration of available transcriptomic datasets revealed IL-6, NFKB1, and PIK3CG expression along with the IL-17 signaling pathway as top candidate molecular linkage mechanisms between peri-implantitis and T2DM.
Project description:The initiation of atopic dermatitis (AD) typically happens very early in life, but most of our understanding of AD is derived from studies on AD patients in adult. The aim of the present study was to identify gene signature speficic to pediatric AD comapred with adult AD. The gene expression profiles of four datasets (GSE32924, GSE36842, GSE58558, and GSE107361) were downloaded from the GEO database. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein-protein interaction (PPI) network was constructed by Cytoscape software. Total 654 differentially expressed genes (DEGs) (394 up-regulated and 260 down-regulated) were identified in pediatric AD samples with adult AD samples as control. The up-regulated DEGs were significantly enriched in the migration and chemotaxis of granulocyte and neutrophil, while down-regulated DEGs were significantly enriched in biological adhesion. KEGG pathway analysis showed that up-regulated DEGs participated in chemokine signaling pathway while down-regulated DEGs participated in adherens junction, focal adhesion, and regulation of actin cytoskeleton. The top 10 hub genes GAPDH, EGFR, ACTB, ESR1, CDK1, CXCL8, CD44, KRAS, PTGS2, and SMC3 were involved in chemokine signaling pathway, cytokine-cytokine receptor interaction, interleukin-17 signaling pathway, and regulation of actin cytoskeleton. In conclusion, we identified DEGs and hub genes involved in pediatric AD, which might be used as therapeutic targets and diagnostic biomarkers for pediatric AD.
Project description:The high mortality of colorectal cancer (CRC) patients and the limitations of conventional tumor-node-metastasis (TNM) stage emphasized the necessity of exploring hub genes closely related to carcinogenesis and prognosis in CRC. The study is aimed at identifying hub genes associated with carcinogenesis and prognosis for CRC. We identified and validated 212 differentially expressed genes (DEGs) from six Gene Expression Omnibus (GEO) datasets and the Cancer Genome Atlas (TCGA) database. We investigated functional enrichment analysis for DEGs. The protein-protein interaction (PPI) network was constructed, and hub modules and genes in CRC carcinogenesis were extracted. A prognostic signature was developed and validated based on Cox proportional hazards regression analysis. The DEGs mainly regulated biological processes covering response to stimulus, metabolic process, and affected molecular functions containing protein binding and catalytic activity. The DEGs played important roles in CRC-related pathways involving in preneoplastic lesions, carcinogenesis, metastasis, and poor prognosis. Hub genes closely related to CRC carcinogenesis were extracted including six genes in model 1 (CXCL1, CXCL3, CXCL8, CXCL11, NMU, and PPBP) and two genes and Metallothioneins (MTs) in model 2 (SLC26A3 and SLC30A10). Among them, CXCL8 was also related to prognosis. An eight-gene signature was proposed comprising AMH, WBSCR28, SFTA2, MYH2, POU4F1, SIX4, PGPEP1L, and PAX5. The study identified hub genes in CRC carcinogenesis and proposed an eight-gene signature with good reproducibility and robustness at the molecular level for CRC, which might provide directive significance for treatment selection and survival prediction.
Project description:Background:Acute myocardial infarction (AMI) is a common disease with high morbidity and mortality around the world. The aim of this research was to determine the differentially expressed genes (DEGs), which may serve as potential therapeutic targets or new biomarkers in AMI. Methods:From the Gene Expression Omnibus (GEO) database, three gene expression profiles (GSE775, GSE19322, and GSE97494) were downloaded. To identify the DEGs, integrated bioinformatics analysis and robust rank aggregation (RRA) method were applied. These DEGs were performed through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses by using Clusterprofiler package. In order to explore the correlation between these DEGs, the interaction network of protein-protein internet (PPI) was constructed using the STRING database. Utilizing the MCODE plug-in of Cytoscape, the module analysis was performed. Utilizing the cytoHubba plug-in, the hub genes were screened out. Results:57 DEGs in total were identified, including 2 down- and 55 upregulated genes. These DEGs were mainly enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and so on. The module analysis filtered out 18 key genes, including Cxcl5, Arg1, Cxcl1, Spp1, Selp, Ptx3, Tnfaip6, Mmp8, Serpine1, Ptgs2, Il6, Il1r2, Il1b, Ccl3, Ccr1, Hmox1, Cxcl2, and Ccl2. Ccr1 was the most fundamental gene in PPI network. 4 hub genes in total were identified, including Cxcl1, Cxcl2, Cxcl5, and Mmp8. Conclusion:This study may provide credible molecular biomarkers in terms of screening, diagnosis, and prognosis for AMI. Meanwhile, it also serves as a basis for exploring new therapeutic target for AMI.