Identification of key genes in coronary artery disease: an integrative approach based on weighted gene co-expression network analysis and their correlation with immune infiltration.
Ontology highlight
ABSTRACT:
SUBMITTER: Yang Y
PROVIDER: S-EPMC8034924 | biostudies-literature |
REPOSITORIES: biostudies-literature
Similar Datasets
Project description:Background: Coronary artery disease (CAD) exerts a global challenge to public health. Genetic heritability is one of the most vital contributing factors in the pathophysiology of CAD. Co-expression network analysis is an applicable and robust method for the interpretation of biological interaction from microarray data. Previous CAD studies have focused on peripheral blood samples since the processes of CAD may vary from tissue to blood. It is therefore necessary to find biomarkers for CAD in heart tissues; their association also requires further illustration. Materials and Methods: To filter for causal genes, an analysis of microarray expression profiles, GSE12504 and GSE22253, was performed with weighted gene co-expression network analysis (WGCNA). Co-expression modules were constructed after batch effect removal and data normalization. The results showed that 7 co-expression modules with 8,525 genes and 1,210 differentially expressed genes (DEGs) were identified. Furthermore, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Four major pathways in CAD tissue and hub genes were addressed in the Hybrid Mouse Diversity Panel (HMDP) and Human Protein Atlas (HPA), and isoproterenol (ISO)/doxycycline (DOX)-induced heart toxicity models were used to validate the hub genes. Lastly, the hub genes and risk variants were verified in the CAD cohort and in genome-wide association studies (GWAS). Results: The results showed that RNF181 and eight other hub genes are perturbed during CAD in heart tissues. Additionally, the expression of RNF181 was validated using RT-PCR and immunohistochemistry (IHC) staining in two cardiotoxicity mouse models. The association was further verified in the CAD patient cohort and in GWAS. Conclusion: Our findings illustrated for the first time that the E3 ubiquitination ligase protein RNF181 may serve as a potential biomarker in CAD, but further in vivo validation is warranted.
| S-EPMC8867041 | biostudies-literature
Project description:BackgroundCoronary heart disease (CHD) is the most prevalent disease with an unelucidated pathogenetic mechanism and is mediated by complex molecular interactions of exosomes. Here, we aimed to identify differentially expressed exosome genes for the disease development and prognosis of CHD.MethodSix CHD samples and 32 normal samples were downloaded from the exoRbase database to identify the candidate genes in the CHD. The differentially expressed genes (DEGs) were identified. And then, weighted gene correlation network analysis (WGCNA) was used to investigate the modules in coexpressed genes between CHD samples and normal samples. DEGs and the module of the WGCNA were intersected to obtain the most relevant exosome genes. After that, the function enrichment analyses and protein-protein interaction network (PPI) were performed for the particular module using STRING and Cytoscape software. Finally, the CIBERSORT algorithm was used to analyze the immune infiltration of exosome genes between CHD samples and normal samples.ResultWe obtain a total of 715 overlapping exosome genes located at the intersection of the DEGs and key modules. The Gene Ontology enrichment of DEGs in the blue module included inflammatory response, neutrophil degranulation, and activation of CHD. In addition, protein-protein networks were constructed, and hub genes were identified, such as LYZ, CAMP, HP, ORM1, and LTF. The immune infiltration profiles varied significantly between normal controls and CHD. Finally, we found that mast cells activated and eosinophils had a positive correlation. B cell memory had a significant negative correlation with B cell naive. Besides, neutrophils and mast cells were significantly increased in CHD patients.ConclusionThe underlying mechanism may be related to neutrophil degranulation and the immune response. The hub genes and the difference in immune infiltration identified in the present study may provide new insights into the diagnostic and provide candidate targets for CHD.
| S-EPMC8536412 | biostudies-literature
Project description:Vestibular schwannomas are the most common tumors of the cerebellopontine angle, but their pathogenesis is still unclear. This study aimed to explore the molecular mechanisms and potential therapeutic target biomarkers in vestibular schwannoma. Two datasets (GSE141801 and GSE54934) were downloaded from the Gene Expression Omnibus database. Weighted gene coexpression network analysis was performed to find the key modules associated with vestibular schwannoma (VS). Functional enrichment analysis was applied to evaluate the gene enrichment signaling pathway in key modules. Protein-protein interaction networks in key modules were constructed using the STRING website. Hub genes were identified by intersecting candidate hub genes in protein-protein interaction network and candidate hub genes in key modules. Single-sample gene set enrichment analysis was utilized to quantify the abundance of tumor-infiltrating immune cells in VSs and normal control nerves. A Random forest classifier was developed based on hub genes identified in this study and validated on an independent dataset (GSE108524). Results of immune cell infiltration were also validated on GSE108524 by gene set enrichment analysis. Eight genes from coexpression modules were identified as hub genes, that is, CCND1, CAV1, GLI1, SOX9, LY86, TLR3, TREM2, and C3AR1, which might be potential therapeutic targets for VS. We also found that there were distinct differences in the infiltration levels of immune cells between VSs and normal control nerves. Overall, our findings may be useful for investigating the mechanisms underlying VS and provide noteworthy directions for future research.
| S-EPMC10082262 | 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:Stroke is one of the leading causes of death and disability worldwide. Evidence shows that ischemic stroke (IS) accounts for nearly 80 percent of all strokes and that the etiology, risk factors, and prognosis of this disease differ by gender. Female patients may bear a greater burden than male patients. The immune system may play an important role in the pathophysiology of females with IS. Therefore, it is critical to investigate the key biomarkers and immune infiltration of female IS patients to develop effective treatment methods. Herein, we used weighted gene co-expression network analysis (WGCNA) to determine the key modules and core genes in female IS patients using the GSE22255, GSE37587, and GSE16561 datasets from the GEO database. Subsequently, we performed functional enrichment analysis and built a protein-protein interaction (PPI) network. Ten genes were selected as the true central genes for further investigation. After that, we explored the specific molecular and biological functions of these hub genes to gain a better understanding of the underlying pathogenesis of female IS patients. Moreover, the "Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)" was used to examine the distribution pattern of immune subtypes in female patients with IS and normal controls, revealing a new potential target for clinical treatment of the disease.
| S-EPMC9012649 | biostudies-literature
Project description:Bronchopulmonary dysplasia (BPD) is a complex disorder resulting from interactions between genes and the environment. The accurate molecular etiology of BPD remains largely unclear. This study aimed to identify key BPD-associated genes and pathways functionally enriched using weighted gene co-expression network analysis (WGCNA). We analyzed microarray data of 62 pre-term patients with BPD and 38 pre-term patients without BPD from Gene Expression Omnibus (GEO). WGCNA was used to construct a gene expression network, and genes were classified into definite modules. In addition, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of BPD-related hub genes were performed. Firstly, we constructed a weighted gene co-expression network, and genes were divided into 10 modules. Among the modules, the yellow module was related to BPD progression and severity and included the following hub genes: MMP25, MMP9, SIRPA, CKAP4, SLCO4C1, and SLC2A3; and the red module included some co-expression molecules that displayed a continuous decline in expression with BPD progression and included the following hub genes: LEF1, ITK, CD6, RASGRP1, IL7R, SKAP1, CD3E, and ICOS. GO and KEGG analyses showed that high expression of inflammatory response-related genes and low expression of T cell receptor activation-related genes are significantly correlated with BPD progression. The present WGCNA-based study thus provides an overall perspective of BPD and lays the foundation for identifying potential pathways and hub genes that contribute to the development of BPD.
| S-EPMC7509191 | biostudies-literature
Project description:The current research attempted to identify possible hub genes and pathways of coronary artery disease (CAD) and to detect the possible mechanisms. Array data from GSE90074 were downloaded from the Gene Expression Omnibus (GEO) database. Integrated weighted gene co-expression network analysis (WGCNA) was performed to analyze the gene module and clinical characteristics. Gene Ontology annotation (GO), Disease Ontology (DO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed by clusterProfiler and the DOSE package in R. A protein-protein interaction (PPI) network was established using Cytoscape software, and significant modules were analyzed using Molecular Complex Detection (MCODE) to identify hub genes. Then, further functional validation of hub genes in other microarrays and population samples was performed, and survival analysis was performed to investigate the prognosis. A total of 660 genes were located in three modules and associated with CAD. GO functions identified 484 biological processes, 39 cellular components, and 22 molecular functions with an adjusted P < 0.05. In total, 38 pathways were enriched in KEGG pathway analysis, and 147 DO items were identified with an adjusted P < 0.05 (false discovery rate, FDR set at < 0.05). There was a total of four modules with a score > 10 after PPI network analysis using the MCODE app, and two hub genes (TLR2 and CD14) were identified. Then, we validated the information from the GSE60993 dataset using the GSE59867 dataset and population samples, and we found that these two genes were associated with plaque vulnerability. These two genes varied at different time points after myocardial infarction, and both of them had the lowest prognosis of heart failure when they were expressed at low levels. We performed an integrated WGCNA and validated that TLR2 and CD14 were closely associated with the severity of coronary artery disease, plaque instability and the prognosis of heart failure after myocardial infarction.
| S-EPMC7870792 | biostudies-literature
Project description:The present study sought to identify potential hub genes and pathways of acute coronary syndrome (ACS). We downloaded the dataset (GSE56045) from the Gene Expression Omnibus (GEO) database and analyzed weighted gene coexpression networks (WGCNA). Gene Ontology annotation, Disease Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using R software. The protein-protein interaction (PPI) network was constructed using Cytoscape, and the Molecular Complex Detection app was employed to identify significant modules and hub genes. The hub genes were then validated in other microarrays and patients by RT-PCR. Two modules were identified and associated with coronary artery disease (CAD) and included 219 genes. After function and PPI analyses, 24 genes were identified to be potentially associated with CAD. Linear correlation was performed to calculate the relationship between the gene expression levels and coronary artery calcification score and found that CCR7 (R = -0.081, P = 0.0065), CD2 (R = -0.075, P = 0.0012), CXCR5 (R = -0.065, P = 0.029) and IL7R (R = -0.06, P = 0.043) should be validated in other dataset. By comparing the gene expression levels in different groups in GSE23561, GSE34822, GSE59867, GSE60993 and GSE129935, only two genes (CCR7 and CXCR5) showed significance. The nomogram showed that CXCR5 showed the risk of ACS. Further analysis in chest patients found CXCR5 played a key role resulting in ACS. Our WGCNA analysis identified CXCR5 as a risk factor for ACS, and the potential pathogenesis may be associated with immune inflammation.
| S-EPMC7732301 | biostudies-literature
Project description:BackgroundRupture of intracranial aneurysm (IA) is the leading cause of subarachnoid hemorrhage. However, there are few pharmacological therapies available for the prevention of IA rupture. Therefore, exploring the molecular mechanisms which underlie IA rupture and identifying the potential molecular targets for preventing the rupture of IA is of vital importance.MethodsWe used the Gene Expression Omnibus (GEO) datasets GSE13353, GSE15629, and GSE54083 in our study. The 3 datasets were merged and normalized. Differentially expressed gene (DEG) screening and weighted correlation network analysis (WGCNA) were conducted. The co-expression patterns between ruptured IA samples and unruptured IA samples were compared. Then, the DEGs were mapped into the whole co-expression network of ruptured IA samples, and a DEG co-expression network was generated. Molecular Complex Detection (MCODE) (http://baderlab.org/Software/MCODE) was used to identify key genes based on the DEG co-expression network. Finally, key genes were validated using another GEO dataset (GSE122897), and their potential diagnostic values were shown using receiver operating characteristic (ROC) analysis.ResultsIn our study, 49 DEGs were screened while 8 and 6 gene modules were detected based on ruptured IA samples and unruptured IA samples, respectively. Pathways associated with inflammation and immune response were clustered in the salmon module of ruptured IA samples. The DEG co-expression network with 35 nodes and 168 edges was generated, and 14 key genes were identified based on this DEG co-expression network. The gene with the highest degree in the key gene cluster was CXCR4. All key genes were validated using GSE122897, and they all showed the potential diagnostic value in predicting IA rupture.ConclusionsUsing a weighted gene co-expression network approach, we identified 8 and 6 modules for ruptured IA and unruptured IA, respectively. After that, we identified the hub genes for each module and key genes based on the DEG co-expression network. All these key genes were validated by another GEO dataset and might serve as potential targets for pharmacological therapies and diagnostic markers in predicting IA rupture. Further studies are needed to elucidate the detailed molecular mechanisms and biological functions of these key genes which underlie the rupture of IA.
| S-EPMC7723540 | biostudies-literature