scholarly journals Weighted Gene Co-expression Network Analysis Identifies Crucial Genes Mediating Progression of Carotid Plaque

2021 ◽  
Vol 12 ◽  
Author(s):  
Mengyin Chen ◽  
Siliang Chen ◽  
Dan Yang ◽  
Jiawei Zhou ◽  
Bao Liu ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Network Analysis ◽  
Carotid Plaque ◽  
Carotid Atherosclerosis ◽  
Rho Gtpase ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gtpase Activating Protein ◽  
Iq Motif ◽  
Black Module

BackgroundSurface rupture of carotid plaque can cause severe cerebrovascular disease, including transient ischemic attack and stroke. The aim of this study was to elucidate the molecular mechanism governing carotid plaque progression and to provide candidate treatment targets for carotid atherosclerosis.MethodsThe microarray dataset GSE28829 and the RNA-seq dataset GSE104140, which contain advanced plaque and early plaque samples, were utilized in our analysis. Differentially expressed genes (DEGs) were screened using the “limma” R package. Gene modules for both early and advanced plaques were identified based on co-expression networks constructed by weighted gene co-expression network analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes Genomes (KEGG) analyses were employed in each module. In addition, hub genes for each module were identified. Crucial genes were identified by molecular complex detection (MCODE) based on the DEG co-expression network and were validated by the GSE43292 dataset. Gene set enrichment analysis (GSEA) for crucial genes was performed. Sensitivity analysis was performed to evaluate the robustness of the networks that we constructed.ResultsA total of 436 DEGs were screened, of which 335 were up-regulated and 81 were down-regulated. The pathways related to inflammation and immune response were determined to be concentrated in the black module of the advanced plaques. The hub gene of the black module was ARHGAP18 (Rho GTPase activating protein 18). NCF2 (neutrophil cytosolic factor 2), IQGAP2 (IQ motif containing GTPase activating protein 2) and CD86 (CD86 molecule) had the highest connectivity among the crucial genes. All crucial genes were validated successfully, and sensitivity analysis demonstrated that our results were reliable.ConclusionTo the best of our knowledge, this study is the first to combine DEGs and WGCNA to establish a DEG co-expression network in carotid plaques, and it proposes potential therapeutic targets for carotid atherosclerosis.

scholarly journals Identification of IL10RA by Weighted Correlation Network Analysis and in vitro Validation of Its Association With Prognosis of Metastatic Melanoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Si Cheng ◽  
Zhe Li ◽  
Wenhao Zhang ◽  
Zhiqiang Sun ◽  
Zhigang Fan ◽  
...  
Keyword(s):  
Network Analysis ◽  
Metastatic Melanoma ◽  
Large Scale ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Migration And Invasion

Skin cutaneous melanoma (SKCM) is the major cause of death for skin cancer patients, its high metastasis often leads to poor prognosis of patients with malignant melanoma. However, the molecular mechanisms underlying metastatic melanoma remain to be elucidated. In this study we aim to identify and validate prognostic biomarkers associated with metastatic melanoma. We first construct a co-expression network using large-scale public gene expression profiles from GEO, from which candidate genes are screened out using weighted gene co-expression network analysis (WGCNA). A total of eight modules are established via the average linkage hierarchical clustering, and 111 hub genes are identified from the clinically significant modules. Next, two other datasets from GEO and TCGA are used for further screening of biomarker genes related to prognosis of metastatic melanoma, and identified 11 key genes via survival analysis. We find that IL10RA has the highest correlation with clinically important modules among all identified biomarker genes. Further in vitro biochemical experiments, including CCK8 assays, wound-healing assays and transwell assays, have verified that IL10RA can significantly inhibit the proliferation, migration and invasion of melanoma cells. Furthermore, gene set enrichment analysis shows that PI3K-AKT signaling pathway is significantly enriched in metastatic melanoma with highly expressed IL10RA, indicating that IL10RA mediates in metastatic melanoma via PI3K-AKT pathway.

scholarly journals Identification of Crucial Genes and Infiltrating Immune Cells Underlying Sepsis-Induced Cardiomyopathy via Weighted Gene Co-Expression Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Juexing Li ◽  
Lei Zhou ◽  
Zhenhua Li ◽  
Shangneng Yang ◽  
Liangyue Tang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Immune Cells ◽  
Plasma Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Cardiac Metabolism ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Research Studies

Sepsis-induced cardiomyopathy (SIC), with a possibly reversible cardiac dysfunction, is a potential complication of septic shock. Despite quite a few mechanisms including the inflammatory mediator, exosomes, and mitochondrial dysfunction, having been confirmed in the existing research studies we still find it obscure about the overall situation of gene co-expression that how they can affect the pathological process of SIC. Thus, we intended to find out the crucial hub genes, biological signaling pathways, and infiltration of immunocytes underlying SIC. It was weighted gene co-expression network analysis that worked as our major method on the ground of the gene expression profiles: hearts of those who died from sepsis were compared to hearts donated by non-failing humans which could not be transplanted for technical reasons (GSE79962). The top 25 percent of variant genes were abstracted to identify 10 co-expression modules. In these modules, brown and green modules showed the strongest negative and positive correlation with SIC, which were primarily enriched in the bioenergy metabolism, immunoreaction, and cell death. Next, nine genes (LRRC39, COQ10A, FSD2, PPP1R3A, TNFRSF11B, IL1RAP, DGKD, POR, and THBS1) including two downregulated and seven upregulated genes which were chosen as hub genes that meant the expressive level of which was higher than the counterparts in control groups. Then, the gene set enrichment analysis (GSEA) demonstrated a close relationship of hub genes to the cardiac metabolism and the necroptosis and apoptosis of cells in SIC. Concerning immune cells infiltration, a higher level of neutrophils and B cells native and a lower level of mast cells resting and plasma cells had been observed in patients with SIC. In general, nine candidate biomarkers were authenticated as a reliable signature for deeper exploration of basic and clinical research studies on SIC.

Identification of IL-6 as a potential mediator of the myocardial fibrosis that occurs in response to surgery with cardiopulmonary bypass in children with Tetralogy of Fallot

2021 ◽  
pp. 1-7
Author(s):  
Hongtao Liu ◽  
Yun Zhang ◽  
Zhenhai Wu ◽  
Liangqing Zhang
Keyword(s):  
Heart Failure ◽  
Network Analysis ◽  
Myocardial Fibrosis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Gene Correlation ◽  
Cell Expression

Abstract Background: Tetralogy of Fallot is a common CHD. Studies have shown a close link between heart failure and myocardial fibrosis. Interleukin-6 has been suggested to be a post-independent factor of heart failure. This study aimed to explore the relationship between IL-6 and myocardial fibrosis during cardiopulmonary bypass. Material and Methods: We downloaded the expression profile dataset GSE132176 from Gene Expression Omnibus. After normalising the raw data, Gene Set Enrichment Analysis and differential gene expression analysis were performed using R. Further, a weighted gene correlation network analysis and a protein–protein interaction network analysis were used to identify HUB genes. Finally, we downloaded single-cell expression data for HUB genes using PanglaoDB. Results: There were 119 differentially expressed genes in right atrium tissues comparing the post-CPB group with the pre-CPB group. IL-6 was found to be significantly up-regulated in the post-CPB group. Six genes (JUN, FOS, ATF3, EGR1, IL-6, and PTGS2) were identified as HUB genes by a weighted gene correlation network analysis and a protein–protein interaction network analysis. Gene Set Enrichment Analysis showed that IL-6 affects the myocardium during CPB mainly through the JAK/STAT signalling pathway. Finally, we used PanglaoDB data to analyse the single-cell expression of the HUB genes. Conclusion: Our findings suggest that high expression of IL-6 and the activation of the JAK/STAT signalling pathway during CPB maybe the potential mechanism of myocardial fibrosis. We speculate that the high expression of IL-6 might be an important factor leading to heart failure after ToF surgery. We expect that these findings will provide a basis for the development of targeted drugs.

scholarly journals Identification of Infertility-Associated Topologically Important Genes Using Weighted Co-expression Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Jingni Wu ◽  
Xiaomeng Xia ◽  
Ye Hu ◽  
Xiaoling Fang ◽  
Sandra Orsulic
Keyword(s):  
Network Analysis ◽  
Topological Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Messenger Rnas ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Highly Correlated

Endometriosis has been associated with a high risk of infertility. However, the underlying molecular mechanism of infertility in endometriosis remains poorly understood. In our study, we aimed to discover topologically important genes related to infertility in endometriosis, based on the structure network mining. We used microarray data from the Gene Expression Omnibus (GEO) database to construct a weighted gene co-expression network for fertile and infertile women with endometriosis and to identify gene modules highly correlated with clinical features of infertility in endometriosis. Additionally, the protein–protein interaction network analysis was used to identify the potential 20 hub messenger RNAs (mRNAs) while the network topological analysis was used to identify nine candidate long non-coding RNAs (lncRNAs). Functional annotations of clinically significant modules and lncRNAs revealed that hub genes might be involved in infertility in endometriosis by regulating G protein-coupled receptor signaling (GPCR) activity. Gene Set Enrichment Analysis showed that the phospholipase C-activating GPCR signaling pathway is correlated with infertility in patients with endometriosis. Taken together, our analysis has identified 29 hub genes which might lead to infertility in endometriosis through the regulation of the GPCR network.

scholarly journals Gene Correlation Network Analysis To Identify Regulatory Factors In Sciatic Nerve Injury

2021 ◽  
Author(s):  
Liuxun Li ◽  
Xiaokang Du ◽  
Haiqian Ling ◽  
Yuhang Li ◽  
Xuemin Wu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Sciatic Nerve ◽  
Nerve Injury ◽  
Enrichment Analysis ◽  
Sciatic Nerve Injury ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Adult Group ◽  
Hub Genes

Abstract Background: Sciatic nerve injury (SNI), which frequently occurs under the traumatic hip and hip fracture dislocation, induces serious complications such as motor and sensory loss, muscle atrophy, or even disabling. The present work aimed to determine the regulating factors and gene network related to the SNI pathology.Methods: Sciatic nerve injury dataset GSE18803 with 24 samples was randomly divided into adult group and neonate group. We performed weighted gene co-expression network analysis (WGCNA) to identify modules associated with SNI in the two groups. Moreover, differentially expressed genes (DEGs) were determined from every group, separately. Subsequently, co-expression network, protein-protein interaction (PPI) network, enrichment analysis and gene set enrichment analysis (GSEA) were integrated to identify hub genes and associated pathways. GSE30165 was used as the test set for investigating the hub gene involvement within SNI. Finally, we employed DGIdb for predicting the possible therapeutic agents leading to the abnormal up-regulation of hub genes.Results: 14 SNI status modules and 97 DEGs were identified in adult group, while 15 modules and 21 DEGs in neonate group. A total of 12 hub genes was overlapping from co-expression and PPI network. After the results from both test and training sets were overlapped, we verified that the ten real hub genes showed remarkably up-regulation within SNI. According to functional enrichment of DEGs, the above genes participated in the immune effector process, inflammatory responses, the antigen processing and presentation, and the phagocytosis. GSEA also supported that gene sets with the highest significance was mostly related to the cytokine-cytokine receptor interaction.Conclusions: The gene expression network is determined in the present work based on the related regulating factors within SNI, which sheds more lights on SNI pathology and offers the possible biomarkers and therapeutic targets in subsequent research.

scholarly journals A Class of Protein-Coding RNAs Binds to Polycomb Repressive Complex 2 and Alters Histone Methylation

2021 ◽  
Vol 11 ◽  
Author(s):  
Meijian Liao ◽  
Xiaolin Sun ◽  
Shoucui Gao ◽  
Yaou Zhang
Keyword(s):  
Network Analysis ◽  
Transcriptional Repression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Rna Recognition Motif ◽  
Rna Recognition ◽  
Polycomb Repressive Complex ◽  
Protein Coding ◽  
Polycomb Repressive Complex 2 ◽  
Rna Immunoprecipitation

Polycomb repressive complex 2 (PRC2) is a multi-subunit protein complex mediating the methylation of lysine 27 on histone H3 and playing an important role in transcriptional repression during tumorigenesis and development. Previous studies revealed that both protein-coding and non-coding RNAs could bind to PRC2 complex. However, the functions of protein-coding RNAs that bind to PRC2 complex in tumor are still unknown. Through data mining and RNA immunoprecipitation (RIP) assay, our study found that there were a class of protein-coding RNAs bound to PRC2 complex and H3 with tri-methylation on lysine 27. The Bayesian gene regulatory network analysis pointed out that these RNAs regulated the expression of PRC2-regulated genes in cancer. In addition, gene set enrichment analysis (GSEA), gene ontology (GO) analysis, and weighted gene co-expression network analysis (WGCNA) also confirmed that these RNAs were associated with histone modification in cancer. We also confirmed that MYO1C, a PRC2-bound transcript, inhibited the modification level of H3K27me3. Further detailed study showed that TMEM117 regulated TSLP expression through EZH2-mediated H3K27me3 modification. Interestingly, the RNA recognition motif of PRC2 complex might help these RNAs bind to the PRC2 complex more easily. The same regulatory pattern was found in mice as well.

Abstract 311: Network Analysis of Pathways Associated with Genetic Regulation of Trimethylamine-N-Oxide

2015 ◽  
Vol 35 (suppl_1) ◽  
Author(s):  
Brian Bennett ◽  
Tangi L Smallwood ◽  
Garron Wright ◽  
Meredith Bostrom
Keyword(s):  
Risk Factors ◽  
Network Analysis ◽  
Genetic Regulation ◽  
Enrichment Analysis ◽  
Plasma Lipoproteins ◽  
Mrna Levels ◽  
Nucleotide Polymorphisms ◽  
Mouse Population ◽  
Environmental Interactions ◽  
Black Module

Common complex diseases, such as atherosclerosis, diabetes and obesity, are the result of genetic, environmental and gene/environmental interactions. Genetic reference panels are powerful tools to interrogate how the environment interacts with the underlying genetic architecture to influence disease susceptibility. Recently 2 genetic reference panels have been created: the Diversity Outbred (DO) mouse population and Collaborative Cross (CC). The mice comprising the CC/DO are mosaics of C57BL6/J, A/J, NOD/ShiLtJ, NZO/HiLtJ, WSB/EiJ, CAST/EiJ, PWK/PhJ, and 129S1/SvImJ. Both the DO and CC populations have tremendous genetic diversity, containing approximately 45 million segregating single-nucleotide polymorphisms (SNPs). Our primary goal was to characterize the genetic regulation of risk factors for atherosclerosis susceptibility in the 8 founder strains of the DO/CC reference panels. Using a systems genetic approach, we perform integrative analysis of clinical traits, targeted metabolomics and RNA expression. We quantitated genome wide mRNA levels using RNA sequencing followed by network analysis. The network was then related to clinical traits and a targeted panel of 5 metabolites (trimethylamine-N-oxide (TMAO), Betaine, Choline and Creatinine) to identify key groups of genes (called modules). We performed weighted gene network co-expression analysis (WGCNA) using 17,122 genes across 46 mice (N = 5-8 mice per strain). The network contains 47 independent co-expression modules and associated these modules with plasma lipoproteins, liver triglyceride levels and the novel plasma risk factors TMAO and Betaine. For example, the turquoise module was most strongly associated with TMAO levels (p < 2 x 10-8) and the black module was associated with plasma Betaine concentrations. Both modules contain several candidate genes and enrichment analysis using Database for Annotation, Visualization and Integrated Discovery (DAVID ) v6.7 Studies are ongoing to validate the key molecular drivers of the network with a particular interest in the transcriptional network regulating plasma TMAO and Betaine concentrations.

scholarly journals Four Novel Prognostic Genes Related to Prostate Cancer Identified Using Co-expression Structure Network Analysis

2021 ◽  
Vol 12 ◽  
Author(s):  
Tao Feng ◽  
Dechao Wei ◽  
Qiankun Li ◽  
Xiaobing Yang ◽  
Yili Han ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Network Analysis ◽  
Single Gene ◽  
Characteristic Curve ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
The Cancer Genome Atlas ◽  
Sequencing Data ◽  
Hub Genes ◽  
Gene Set

Prostate cancer (PCa) is one of the most common malignancies for males, but very little is known about its pathogenesis. This study aimed to identify novel biomarkers associated with PCa prognosis and elucidate the underlying molecular mechanism. First, The Cancer Genome Atlas (TCGA) RNA-sequencing data were utilized to identify differentially expressed genes (DEGs) between tumor and normal samples. The DEGs were then applied to construct a co-expression and mined using structure network analysis. The magenta module that was highly related to the Gleason score (r = 0.46, p = 3e–26) and tumor stage (r = 0.38, p = 2e–17) was screened. Subsequently, all genes of the magenta module underwent function annotation. From the key module, CCNA2, CKAP2L, NCAPG, and NUSAP1 were chosen as the four candidate genes. Finally, internal (TCGA) and external data sets (GSE32571, GSE70770, and GSE141551) were combined to validate and predict the value of real hub genes. The results show that the above genes are up-regulated in PCa samples, and higher expression levels show significant association with higher Gleason scores and tumor T stage. Moreover, receiver operating characteristic curve and survival analysis validate the excellent value of hub genes in PCa progression and prognosis. In addition, the protein levels of these four genes also remain higher in tumor tissues when compared with normal tissues. Gene set enrichment analysis and gene set variation analysis for a single gene reveal the close relation with cell proliferation. Meanwhile, 11 small molecular drugs that have the potential to treat PCa were also screened. In conclusion, our research identified four potential prognostic genes and several candidate molecular drugs for treating PCa.

scholarly journals Comprehensive Analysis to Identify Key Genes Involved in Advanced Atherosclerosis

2021 ◽  
Vol 2021 ◽  
pp. 1-25
Author(s):  
Tian-ming Huo ◽  
Zhi-wei Wang
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Lupus Erythematosus ◽  
Inflammatory Responses ◽  
Therapeutic Targets ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Key Genes

Background. The study was aimed at finding accurate and effective therapeutic targets and deepening our understanding of the mechanisms of advanced atherosclerosis (AA). Methods. We downloaded the gene expression datasets GSE28829, GSE120521, and GSE43292 from Gene Expression Omnibus. Weighted gene coexpression network analysis (WGCNA) was performed for GSE28829, and functional enrichment analysis and protein–protein interaction network analysis were conducted on the key module. Significant genes in the key module were analyzed by molecular complex detection, and genes in the most important subnetwork were defined as hub genes. Multiple dataset analyses for hub genes were conducted. Genes that overlapped between hub genes and differentially expressed genes (DEGs) of GSE28829 and GSE120521 were defined as key genes. Further validation for key genes was performed using GSE28829 and GSE43292. Gene set enrichment analysis (GSEA) was applied to key genes. Results. A total of 77 significant genes in the key module of GSE28829 were screened out that were mainly associated with inflammation and immunity. The subnetwork was obtained from significant genes, and 18 genes in this module were defined as hub genes, which were related to immunity and expressed in multiple diseases, particularly systemic lupus erythematosus. Some hub genes were regulated by SPI1 and associated with the blood, spleen, and lung. After overlapping with DEGs of GSE28829 and GSE120521, a total of 10 genes (HCK, ITGAM, CTSS, TYROBP, LAPTM5, FCER1G, ITGB2, NCF2, AIF1, and CD86) were identified as key genes. All key genes were validated and evaluated successfully and were related to immune response pathways. Conclusion. Our study suggests that the key genes related to immune and inflammatory responses are involved in the development of AA. This may deepen our understanding of the mechanisms of and provide valuable therapeutic targets for AA.

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