Weighted Gene Co-Expression Network Analysis Identifies Key Modules and Hub Genes Associated with Mycobacterial Infection of Human Macrophages.
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ABSTRACT:
SUBMITTER: Lu L
PROVIDER: S-EPMC7909288 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundThe purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms.MethodsThe microarray dataset of GSE66676 obtained from patients with hyperlipidaemia was downloaded. Weighted gene co-expression network (WGCNA) analysis was used to analyse the gene expression profile, and the royal blue module was considered to have the highest correlation. Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were implemented for the identification of genes in the royal blue module using the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov ). A protein-protein interaction (PPI) network was established by using the online STRING tool. Then, several hub genes were identified by the MCODE and cytoHubba plug-ins in Cytoscape software.ResultsThe significant module (royal blue) identified was associated with TC, TG and non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royal blue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis pathways of unsaturated fatty acids. SQLE (degree = 17) was revealed as a key molecule associated with hypercholesterolaemia (HCH), and SCD was revealed as a key molecule associated with hypertriglyceridaemia (HTG). RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples.ConclusionsSQLE and SCD are related to hyperlipidaemia, and SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.
| S-EPMC7934476 | biostudies-literature
Project description:BackgroundAsthma is a heterogeneous disease that can be divided into four inflammatory phenotypes: eosinophilic asthma (EA), neutrophilic asthma (NA), mixed granulocytic asthma (MGA), and paucigranulocytic asthma (PGA). While research has mainly focused on EA and NA, the understanding of PGA is limited. In this study, we aimed to identify underlying mechanisms and hub genes of PGA.MethodsBased on the dataset from Gene Expression Omnibus(GEO), weighted gene coexpression network analysis (WGCNA), differentially expressed genes (DEGs) analysis and protein-protein interaction (PPI) network analysis were conducted to construct a gene network and to identify key gene modules and hub genes. Functional enrichment analyses were performed to investigate the biological process, pathways and immune status of PGA. The hub genes were validated in a separate dataset.ResultsCompared to non-PGA, PGA had a different gene expression pattern, in which 449 genes were differentially expressed. One gene module significantly associated with PGA was identified. Intersection between the differentially expressed genes (DEGs) and the genes from the module that were most relevant to PGA were mainly enriched in inflammation and immune response regulation. The single sample Gene Set Enrichment Analysis (ssGSEA) suggested a decreased immune infiltration and function in PGA. Finally six hub genes of PGA were identified, including ADCY2, CXCL1, FPRL1, GPR109B, GPR109A and ADCY3, which were validated in a separate dataset of GSE137268.ConclusionsOur study characterized distinct gene expression patterns, biological processes and immune status of PGA and identified hub genes, which may improve the understanding of underlying mechanism and provide potential therapeutic targets for PGA.
| S-EPMC8565058 | biostudies-literature
Project description:Atrial fibrillation (AF) is the most common form of cardiac arrhythmia and significantly increases the risks of morbidity, mortality and health care expenditure; however, treatment for AF remains unsatisfactory due to the complicated and incompletely understood underlying mechanisms. In the present study, weighted gene co‑expression network analysis (WGCNA) was conducted to identify key modules and hub genes to determine their potential associations with AF. WGCNA was performed in an AF dataset GSE79768 obtained from the Gene Expression Omnibus, which contained data from paired left and right atria in cardiac patients with persistent AF or sinus rhythm. Differentially expressed gene (DEG) analysis was used to supplement and validate the results of WGCNA. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were also performed. Green and magenta modules were identified as the most critical modules associated with AF, from which 6 hub genes, acetyl‑CoA Acetyltransferase 1, death domain‑containing protein CRADD, gypsy retrotransposon integrase 1, FTX transcript, XIST regulator, transcription elongation factor A like 2 and minichromosome maintenance complex component 3 associated protein, were hypothesized to serve key roles in the pathophysiology of AF due to their increased intramodular connectivity. Functional enrichment analysis results demonstrated that the green module was associated with energy metabolism, and the magenta module may be associated with the Hippo pathway and contain multiple interactive pathways associated with apoptosis and inflammation. In addition, the blue module was identified to be an important regulatory module in AF with a higher specificity for the left atria, the genes of which were primarily correlated with complement, coagulation and extracellular matrix formation. These results suggest that may improve understanding of the underlying mechanisms of AF, and assist in identifying biomarkers and potential therapeutic targets for treating patients with AF.
| S-EPMC6984797 | biostudies-literature
Project description:The analysis of the potential molecule targets of coronary artery disease (CAD) is critical for understanding the molecular mechanisms of disease. However, studies of global microarray gene co-expression analysis of CAD still remain limited.Microarray data of CAD (GSE23561) were downloaded from Gene Expression Omnibus, including peripheral blood samples from CAD patients (n = 6) and controls (n = 9). Limma package in R was used to identify the differentially expressed genes (DEGs) between CAD and control samples. Using weighted gene co-expression network analysis (WGCNA) package in R, WGCNA was performed to identify significant modules in the network. Then, functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID software. Moreover, hub genes in the module were analyzed by isubpathwayminer package in R and GenCLiP 2.0 tool to identify the significant sub-pathways.Total 3711 DEGs and 21 modules for them were identified in CAD samples. The most significant module was associated with the pathways of hypertrophic cardiomyopathy and membrane related functions. In addition, the top 30 hub genes with high connectivity in the module were selected, and two genes (G6PD and S100A7) were taken as key molecules via sub-pathway screening and data mining.A module associated with hypertrophic cardiomyopathy pathway was detected in CAD samples. G6PD and S100A7 were the potential targets in CAD. Our finding might provide novel insight into the underlying molecular mechanism of CAD.
| S-EPMC4779223 | biostudies-literature
Project description:This investigation seeks to dissect coronary artery disease molecular target candidates along with its underlying molecular mechanisms. Data on patients with CAD across three separate array data sets, GSE66360, GSE19339 and GSE97320 were extracted. The gene expression profiles were obtained by normalizing and removing the differences between the three data sets, and important modules linked to coronary heart disease were identified using weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) functional and Kyoto Encyclopedia of Genes and genomes (KEGG) pathway enrichment analyses were applied in order to identify statistically significant genetic modules with the Database for Annotation, Visualization and Integrated Discovery (DAVID) online tool (version 6.8; http://david.abcc.ncifcrf.gov ). The online STRING tool was used to construct a protein-protein interaction (PPI) network, followed by the use of Molecular Complex Detection (MCODE) plug-ins in Cytoscape software to identify hub genes. Two significant modules (green-yellow and magenta) were identified in the CAD samples. Genes in the magenta module were noted to be involved in inflammatory and immune-related pathways, based on GO and KEGG enrichment analyses. After the MCODE analysis, two different MCODE complexes were identified in the magenta module, and four hub genes (ITGAM, degree = 39; CAMP, degree = 37; TYROBP, degree = 28; ICAM1, degree = 18) were uncovered to be critical players in mediating CAD. Independent verification data as well as our RT-qPCR results were highly consistent with the above finding. ITGAM, CAMP, TYROBP and ICAM1 are potential targets in CAD. The underlying mechanism may be related to the transendothelial migration of leukocytes and the immune response.
| S-EPMC7988178 | biostudies-literature
Project description:Introduction: Acute myeloid leukemia (AML) is the most common type of leukemia in adults. However, there is a gap in understanding the molecular basis of the disease, partly because key genes associated with AML have not been extensively explored. In the current study, we aimed to identify genes that have strong association with AML based on a cross-species integrative approach. Methods: We used Weighted Gene Co-Expression Network Analysis (WGCNA) to identify co-expressed gene modules significantly correlated with human AML, and further selected the genes exhibiting a significant difference in expression between AML and healthy mouse. Protein-protein interactions, transcription factors, gene function, genetic regulation, and coding sequence variants were integrated to identify key hub genes in AML. Results: The cross-species approach identified a total of 412 genes associated with both human and mouse AML. Enrichment analysis confirmed an association of these genes with hematopoietic and immune-related functions, phenotypes, processes, and pathways. Further, the integrated analysis approach identified a set of important module genes including Nfe2, Trim27, Mef2c, Ets1, Tal1, Foxo1, and Gata1 in AML. Six of these genes (except ETS1) showed significant differential expression between human AML and healthy samples in an independent microarray dataset. All of these genes are known to be involved in immune/hematopoietic functions, and in transcriptional regulation. In addition, Nfe2, Trim27, Mef2c, and Ets1 harbor coding sequence variants, whereas Nfe2 and Trim27 are cis-regulated, making them attractive candidates for validation. Furthermore, subtype-specific analysis of the hub genes in human AML indicated high expression of NFE2 across all the subtypes (M0 through M7) and enriched expression of ETS1, LEF1, GATA1, and TAL1 in M6 and M7 subtypes. A significant correlation between methylation status and expression level was observed for most of these genes in AML patients. Conclusion: Findings from the current study highlight the importance of our cross-species approach in the identification of multiple key candidate genes in AML, which can be further studied to explore their detailed role in leukemia/AML.
| S-EPMC10008864 | biostudies-literature
Project description:BackgroundTongue squamous cell carcinoma (TSCC) is one of the most common malignant tumors in head and neck, but its molecular mechanism is not clear.MethodsWeighted gene co-expression network analysis (WGCNA) combining with gene differential expression analysis, survival analysis to screen key modules and hub genes related to the progress of TSCC. Gene Set Enrichment Analysis (GSEA) was used to identify biological pathways that might be involved.ResultsWeighted gene co-expression network was constructed based on dataset GSE34105. The blue module and turquoise module most related to the progress of TSCC were identified by the network. Gene Ontology (GO) enrichment analysis showed that 2 key modules were significantly enriched in apoptosis and immunity related biological processes and pathway. Network topology analysis, gene difference analysis and survival analysis were used to screen 9 hub genes (NOC2L, AIMP2, ANXA2, DIABLO, H2AFZ, MANBAL, PRDX6, SNX14, TIMM23). The expression of hub genes was significantly correlated with the prognosis of TSCC. GSEA showed that the high expression group of hub genes was mainly enriched in olfactory transduction, neuroactive ligand receptor interaction, nicotinate and nicotinamide metabolism, and the low expression group was mainly enriched in base excision repair, cysteine and methionine metabolism, oxidative phosphorylation.ConclusionTwo key modules and 9 hub genes screened by WGCNA were closely related to the occurrence and prognosis of TSCC. Hub genes can be used as biomarkers and potential therapeutic targets for the accurate diagnosis and treatment of TSCC in the future.
| S-EPMC6750333 | biostudies-literature
Project description:The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug-target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.
| S-EPMC8397209 | biostudies-literature
Project description:BackgroundFat deposition is an important economic trait in livestock and poultry production. However, the relationship between various genes and signal pathways of fat deposition is still unclear to a large extent. The purpose of this study is to analyze the potential molecular targets and related molecular pathways in bovine subcutaneous adipose tissue.ResultsWe downloaded the GSE116775 microarray dataset from Gene Expression Omnibus (GEO). The weighted gene co-expression network (WGCNA) was used to analyze the gene expression profile, and the key gene modules with the highest correlation with subcutaneous adipose tissue were identified, and the functional enrichment of the key modules was analyzed. Then, the "real" Hub gene was screened by in-module analysis and protein-protein interaction network (PPI), and its expression level in tissue samples and adipocytes was verified. The study showed that a total of nine co-expression modules were identified, and the number of genes in these modules ranged from 101 to 1,509. Among them, the blue module is most closely related to subcutaneous adipose tissue, containing 1,387 genes. These genes were significantly enriched in 10 gene ontologies including extracellular matrix organization, biological adhesion, and collagen metabolic process, and were mainly involved in pathways including ECM-receptor interaction, focal adhesion, cAMP signaling pathway, PI3K-AKT signaling pathway, and regulation of lipolysis in adipocytes. In the PPI network and coexpression network, five genes (CAV1, ITGA5, COL5A1, ABL1, and HSPG2) were identified as "real" Hub genes. Analysis of Hub gene expression by dataset revealed that the expression of these Hub genes was significantly higher in subcutaneous adipose tissue than in other tissues. In addition, real-time fluorescence quantitative PCR (qRT-PCR) analysis based on tissue samples and adipocytes also confirmed the above results.ConclusionIn this study, five key genes related to subcutaneous adipose tissue were discovered, which laid a foundation for further study of the molecular regulation mechanism of subcutaneous adipose tissue development and adipose deposition.
| S-EPMC9257221 | biostudies-literature
Project description:PurposeOral squamous cell carcinoma (OSCC) is one of the most common malignant diseases worldwide, yet its molecular mechanisms are largely unknown. We aimed to construct gene co-expression networks to identify key modules and hub genes involved in the pathogenesis of OSCC.Patients and methodsWe used dataset GSE30784 to construct co-expression networks by weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). Hub genes were screened and validated by other datasets.ResultsTurquoise and brown modules were found to be the most significantly related to tumorigenesis. Functional enrichment analysis showed that the turquoise module was associated with cell-cell adhesion, extracellular matrix and collagen catabolic process. A total of 10 hub genes (MMP1, TNFRSF12A, PLAU, FSCN1, PDPN, KRT78, EVPL, GGT6, SMIM5 and CYSRT1) were identified and validated at transcriptional and translational levels. Their genetic alteration and survival analysis were also revealed.ConclusionWe identified two modules and 10 hub genes, which were associated with the tumorigenesis of OSCC. The two modules provided references that will advance the understanding of mechanisms of tumorigenesis in OSCC. Moreover, the hub genes may serve as biomarkers and therapeutic targets for precise diagnosis and treatment of OSCC in the future.
| S-EPMC6157991 | biostudies-literature