The Identification and Validation of Hub Genes Associated with Acute Myocardial Infarction Using Weighted Gene Co-Expression Network Analysis.
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ABSTRACT:
SUBMITTER: Xue J
PROVIDER: S-EPMC8778825 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:To explore potential biomarkers of acute myocardial infarction (AMI) in females by using bioinformatics analysis. In this study, we explored potential biomarkers of AMI in females using bioinformatics analysis. We screened a total of 186 differentially expressed genes from the Gene Expression Omnibus. In the study, we found that weighted gene co-expression network analysis explored the co-expression network of genes and identified key modules. Simultaneously, we chose brown modules as key modules related to AMI. In this study, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis revealed that genes in the brown module were mainly enriched in "heparin" and 'complementation and coagulation cascade. Based on the protein-protein interaction network, we identified S100A9, mitogen-activated protein kinase (MAPK) 3, MAPK1, MMP3, interleukin (IL)-17A, and HSP90AB1 as hub gene sets. Whereas, polymerase chain reaction results showed that S100A9, MAPK3, MAPK1, MMP3, IL-17A, and HSP90AB1 were highly expressed compared with the control group. The IL-17 signaling pathway associated with an inflammatory response may be a potential biomarker and target for the treatment of women with myocardial infarction.
| S-EPMC10145720 | biostudies-literature
Project description:The study aimed to seek potential biomarkers for acute myocardial infarction (AMI) detection and treatment.The dataset GSE48060 was used, consisting of 52 peripheral blood samples (31 AMI samples and 21 normal controls). By limma package, differentially expressed genes (DEGs) between 2 kinds of samples were identified, followed by enrichment analysis, subpathway analysis, protein-protein interaction (PPI) network analysis, and transcription factor network (TFN) analysis. Weighted gene co-expression network analysis was used to further extract key modules relating to AMI, followed by enrichment and TFN analyses. Expression validation was performed via meta-analysis of 2 datasets, GSE22229 and GSE29111.A set of 428 DEGs in AMI were screened out, and the upregulated toll-like receptor (TLR) family genes (TLR1, TLR2, and TLR10) were enriched in wound response, immune response and inflammatory response functions, and downregulated genes (GBP5, CXCL5, GZMA, CCL5, and CCL4) were correlated with immune response. CCL5, GZMA, GZMB, TLR2, and formyl peptide receptor 1 (FPR1) were predicted as crucial nodes in the PPI network. Signal transducer and activator of transcription 1 (STAT1) was the key transcription factor (TF) with multiple targets. The grey module was highly related to AMI. Genes in this module were closely related to regulation of macrophage activation, and spermatogenic leucine zipper 1 (SPZ1) was identified as a TF. Expressions of TLR2 and FPR1 were confirmed via the integrated matrix.Several potential biomarkers for AMI detection were identified, such as GZMB, GBP5, FPR1, TLR2, STAT1, and SPZ1. They might exert their functions via regulation of immune and inflammation responses. Genes in grey module play significant roles in AMI via regulation of macrophage activation.
| S-EPMC5708914 | biostudies-literature
Project description:BackgroundIdentification of potential molecular targets of acute myocardial infarction is crucial to our comprehensive understanding of the disease mechanism. However, studies of gene coexpression analysis via jointing multiple microarray data of acute myocardial infarction still remain restricted.MethodsMicroarray data of acute myocardial infarction (GSE48060, GSE66360, GSE97320, and GSE19339) were downloaded from Gene Expression Omnibus database. Three data sets without heterogeneity (GSE48060, GSE66360, and GSE97320) were subjected to differential expression analysis using MetaDE package. Differentially expressed genes having upper 25% variation across samples were imported in weighted gene coexpression network analysis. Functional and pathway enrichment analyses were conducted for genes in the most significant module using DAVID. The predicted microRNAs to regulate target genes in the most significant module were identified using TargetScan. Moreover, subpathway analyses using iSubpathwayMiner package and GenCLiP 2.0 were performed on hub genes with high connective weight in the most significant module.ResultsA total of 1027 differentially expressed genes and 33 specific modules were screened out between acute myocardial infarction patients and control samples. Ficolin (collagen/fibrinogen domain containing) 1 (FCN1), CD14 molecule (CD14), S100 calcium binding protein A9 (S100A9), and mitochondrial aldehyde dehydrogenase 2 (ALDH2) were identified as critical target molecules; hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential regulators of the expression of the key genes in the two biggest modules.ConclusionsFCN1, CD14, S100A9, ALDH2, hsa-let-7d, hsa-let-7b, hsa-miR-124-3, and hsa-miR-9-1 were identified as potential candidate regulators in acute myocardial infarction. These findings might provide new comprehension into the underlying molecular mechanism of disease.
| S-EPMC6388335 | biostudies-literature
Project description:ObjectiveTo identify the key hub genes in prostate cancer metastasis based on weighted gene co-expression network analysis (WGCNA) and verify the identified genes.MethodsWhole-genome chip data GSE6919 of prostate cancer study were analyzed using principal component analysis (PCA), and the differentially expressed genes (DEGs) were analyzed using R software. WGCNA was performed to construct a gene co-expression network for screening the key genes. TCGA database was used to explore the expressions of the DEGs and their association with the prognosis. To validate the results, we designed siRNA fragments targeting the metastasis-related gene HNRNPA2B1, and observed its effect on growth, apoptosis, clone formation, migration and invasion of prostate cancer cell lines using MTT assay, flow cytometry, clone formation assay, and Transwell assay.ResultsPCA analysis showed obvious clustering of significant DEGs in metastatic cancer group. The modules obtained by WGCNA analysis in metastasis group involved stem cell differentiation, amino acid metabolism and immune response. Further screening of the genes identified 3 genes related with prostate cancer occurrence (BDH1, PAK4 and EXTL3) and another 3 with prostate cancer metastasis (NKTR, CTBP2 and HNRNPA2B1), which were shown to have differential expressions in TCGA database and were correlated with the patient's overall survival. In the cell experiment, PC3 and LNCap cells transfected with the siRNA fragment targeting HNRNPA2B1 showed obvious growth inhibition with increased cell apoptosis, lowered clone formation ability, and suppressed capacities for migration and invasion.ConclusionWe identified 3 hub genes related with the occurrence (BDH1, PAK4 and EXTL3) and another 3 with metastasis of prostate cancer (NKTR, CTBP2 and HNRNPA2B1) using WGCNA, which provides a new approach for studying the regulatory mechanisms of prostate cancer.
| S-EPMC8685695 | biostudies-literature
Project description:Background: The development of heart failure (HF) remains a common complication following an acute myocardial infarction (AMI), and is associated with substantial adverse outcomes. However, the specific predictive biomarkers and candidate therapeutic targets for post-infarction HF have not been fully established. We sought to perform a weighted gene co-expression network analysis (WGCNA) to identify key modules, hub genes, and possible regulatory targets involved in the development of HF following AMI. Methods: Genes exhibiting the most (top 50%) variation in expression levels across samples in a GSE59867 dataset were imported to the WGCNA. Gene Ontology and pathway enrichment analyses were performed on genes identified in the key module by Metascape. Gene regulatory networks were constructed using the microarray probe reannotation and bioinformatics database. Hub genes were screened out from the key module and validated using other datasets. Results: A total of 10,265 most varied genes and six modules were identified between AMI patients who developed HF within 6 months of follow-up and those who did not. Specifically, the blue module was found to be the most significantly related to the development of post-infarction HF. Functional enrichment analysis revealed that the blue module was primarily associated with the inflammatory response, immune system, and apoptosis. Seven transcriptional factors, including SPI1, ZBTB7A, IRF8, PPARG, P65, KLF4, and Fos, were identified as potential regulators of the expression of genes identified in the blue module. Further, non-coding RNAs, including miR-142-3p and LINC00537, were identified as having close interactions with genes from the blue module. A total of six hub genes (BCL3, HCK, PPIF, S100A9, SERPINA1, and TBC1D9B) were identified and validated for their predictive value in identifying future HFs. Conclusions: By using the WGCNA, we provide new insights into the underlying molecular mechanism and molecular markers correlated with HF development following an AMI, which may serve to improve risk stratification, therapeutic decisions, and prognosis prediction in AMI patients.
| S-EPMC6889910 | biostudies-literature
Project description:BackgroundGastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy.MethodsGSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the "least absolute shrinkage and selection operator" (LASSO) logistic regression. Finally, hub genes were validated in the GSE54129 dataset from GEO by supervised learning method artificial neural network (ANN) algorithm.ResultsTwelve modules with strong preservation were identified by using WGCNA methods in training set. Of which, five modules significantly related to gastric cancer were selected as clinically significant modules, and 713 candidate genes were identified from these five modules. Then, ADIPOQ, ARHGAP39, ATAD3A, C1orf95, CWH43, GRIK3, INHBA, RDH12, SCNN1G, SIGLEC11 and LYVE1 were screened as the hub genes. These hub genes successfully differentiated the tumor samples from the healthy tissues in an independent testing set through artificial neural network algorithm with the area under the receiver operating characteristic curve at 0.946.ConclusionsThese hub genes bearing diagnostic and therapeutic values, and our results may provide a novel prospect for the diagnosis and treatment of gastric cancer in the future.
| S-EPMC7938783 | biostudies-literature
Project description:ObjectiveTo investigate the differential expression gene modules and hub genes associated with Alzheimer's disease (AD) by weighted gene co-expression network analysis (WGCNA) and annotate the biological functions of these modules.MethodsWe downloaded transcriptome sequencing data from the GEO database, and according to the correlation of the genes, a gene co-expression network was constructed with the parameter setting of β=8 and a correlation coefficient threshold of 0.85. Pearson correlation test was used to calculate the correlation between the module genes and clinical traits to screen the gene modules significantly associated with AD and identify the hub genes according to the connectivity within modules. GO functional enrichment analysis and KEGG pathway analysis were used to annotate the functions of the modules. A cell model of AD was established in SH-SY5Y cells by Aβ1-42 treatment, and the mRNA expression levels of the hub genes were compared between the Aβ1-42-treated cells and the control cells.ResultsTen gene co-expression modules were constructed based on the correlations of gene expression, in which the brown (r=0.66, P < 0.001) and turquoise modules (r=-0.68, P < 0.001) were significantly correlated with the AD group. Forty-eight genes were identified as the hub genes in the co-expression network. Function annotation revealed that the genes in both modules were mainly enriched in DNA damage and repair pathways and metabolism-related pathways. Differential expression analysis of the genes revealed that the genes DNASE1, TEKT2 and MTSS1L were highly expressed while ACP2, LANCL2 and GMPR2 were lowly expressed in AD group. The results of cell experiment confirmed the up-regulation of DNASE1, TEKT2 and MTSS1L genes and the down-regulation of ACP2, LANCL2, and GMPR2 in Aβ1-42-treated SH-SY5Y cells (P < 0.01).ConclusionThe brown and turquoise modules are closely correlated with AD. The hub genes including MTSS1L, GMPR2, ACP2, ACTG1 and LANCL2 selected from the modules may participate in AD pathogenesis by regulating DNA damage and repair.
| S-EPMC8752417 | biostudies-literature
Project description:Colorectal cancer (CRC) is the third leading cause of death in the world. However, the key roles of most molecules in CRC remain unclear. This study aimed to identify key modules and hub genes associated with the progression of CRC. The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R. by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified, which were associated with clinical traits. Next, we screened hub genes related to the progression of CRC authenticated by The Cancer Genome Atlas (TCGA) and Oncomine databases. Three hub genes (HCLS1, EVI2B, and CD48) were identified, and survival analysis was further performed. Moreover, the results of qPCR and immunohistochemistry staining revealed that HCLS1, EVI2B, and CD48 are tumor suppressor genes. Further, the functional study verified that over-expression of HCLS1, EVI2B, and CD48 can reduce the proliferation, migration, and invasion ability of CRC cells and significantly suppress CRC tumor growth in vivo. In summary, we identified three hub genes that were associated with the progression of CRC that can be applied in treatment.
| S-EPMC7203460 | 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:BackgroundIn the general population, acute myocardial infarction (AMI) represents a significant cause of mortality. This study is aimed at identifying novel diagnostic biomarkers to aid in treating and diagnosing AMI.MethodsThe Gene Expression Omnibus (GEO) database was explored to extract two microarray datasets, GSE66360 and GSE48060, which were subsequently merged into a single cohort. Both AMI and control samples were analyzed for differentially expressed genes (DEGs), which were subsequently subjected to weighed gene coexpression network analysis (WGCNA) to identify the most significant module. Gene Ontology (GO) and pathway analyses subsequently carried out the most significant gene modules along with construction of a protein-protein interaction network (PPI). Cytoscape plugin cytoHubba allowed for the prediction of the top 4 key genes according to the network maximal clique centrality (MCC) algorithm. The expression levels and diagnostic value of the four key genes were additionally verified in the GSE62646 dataset.ResultsA WCGNA analysis revealed 878 DEGs which were clustered into 6 modules. The module with the most significance in AMI was colored blue. Subsequent GO and KEGG pathway enrichment analysis on blue module genes revealed that they were primarily enriched in the inflammation-related pathways. These findings, in combination with PPI and coexpression networks, resulted in the identification of the top four genes by cytoHubba, which included leukocyte immunoglobulin-like receptor B2 (LILRB2), toll-like receptor 2 (TLR2), neutrophil cytosolic factor 2 (NCF2), and S100A9. Among them, LILRB2, NCF2, and S100A9 were validated in the GSE62646 dataset.ConclusionsThe results suggested that LILRB2, NCF2, and S100A9 could be potential gene biomarkers for AMI.
| S-EPMC8418549 | biostudies-literature