Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Hyperlipidemia

Liu, Miao; Yin, Rong; Pan, Shang‐Ling; Yang, Shuo; Dezhai, Yang; Lin, Wei-Xiong

doi:10.1159/000491982

Cited by 35 publications

(28 citation statements)

References 47 publications

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“…The reaction was incubated at 95°C for 15 min, at 95°C for 28 s, at 61°C for 30 s, and at 72°C for 35 s. All reactions were performed in duplicate. GAPDH was used as the internal control [29].…”

Section: Rna Isolation Reverse Transcription (Rt) and Quantitative Pmentioning

confidence: 99%

LncRNA SNHG8 is identified as a key regulator of acute myocardial infarction by RNA-seq analysis

et al. 2019

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BackgroundLong noncoding RNAs (lncRNAs) are involved in numerous physiological functions. However, their mechanisms in acute myocardial infarction (AMI) are not well understood.MethodsWe performed an RNA-seq analysis to explore the molecular mechanism of AMI by constructing a lncRNA-miRNA-mRNA axis based on the ceRNA hypothesis. The target microRNA data were used to design a global AMI triple network. Thereafter, a functional enrichment analysis and clustering topological analyses were conducted by using the triple network. The expression of lncRNA SNHG8, SOCS3 and ICAM1 was measured by qRT-PCR. The prognostic values of lncRNA SNHG8, SOCS3 and ICAM1 were evaluated using a receiver operating characteristic (ROC) curve.ResultsAn AMI lncRNA-miRNA-mRNA network was constructed that included two mRNAs, one miRNA and one lncRNA. After RT-PCR validation of lncRNA SNHG8, SOCS3 and ICAM1 between the AMI and normal samples, only lncRNA SNHG8 had significant diagnostic value for further analysis. The ROC curve showed that SNHG8 presented an AUC of 0.850, while the AUC of SOCS3 was 0.633 and that of ICAM1 was 0.594. After a pairwise comparison, we found that SNHG8 was statistically significant (P SNHG8-ICAM1 = 0.002; P SNHG8-SOCS3 = 0.031). The results of a functional enrichment analysis of the interacting genes and microRNAs showed that the shared lncRNA SNHG8 may be a new factor in AMI.ConclusionsOur investigation of the lncRNA-miRNA-mRNA regulatory networks in AMI revealed a novel lncRNA, lncRNA SNHG8, as a risk factor for AMI and expanded our understanding of the mechanisms involved in the pathogenesis of AMI.

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Section: Rna Isolation Reverse Transcription (Rt) and Quantitative Pmentioning

confidence: 99%

LncRNA SNHG8 is identified as a key regulator of acute myocardial infarction by RNA-seq analysis

et al. 2019

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“…A comprehensive understanding of the potential molecular mechanisms involved in the pathogenesis of hyperlipidaemia is helpful for its prevention and treatment. As a novel and practical approach to the identi cation of hyperlipidaemia susceptibility genes, a microarray analysis using WGCNA may be helpful for the diagnosis of hyperlipidemia [14]. WGCNA could be used to build a scale-free co-expression network of lipids-associated genes by detecting gene-to-gene interactions rather than simply focusing on the differentially expressed genes (DEGs).…”

Section: Discussionmentioning

confidence: 99%

“…Weighted gene co-expression network analysis (WGCNA) is one of the most effective methods of gene co-expression network analysis. Instead of simply identifying the differentially expressed genes, a scale-free network of gene-gene interactions is generated by WGCNA, and several signi cant modules comprised of genes with similar functions could be identi ed by WGCNA; in addition, it can be used to further analyze the correlation between modules and phenotypes or clinical characteristics [14]. Therefore, WGCNA could be utilized to construct a co-expression network and identify signi cant modules in the network, which may help us to illuminate the intrinsic characteristics of hyperlipidaemia and provide new insights into potential genetic biomarkers, signaling pathways and molecular mechanisms involved in hyperlipidaemia.…”

Section: Introductionmentioning

confidence: 99%

Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia

Liao

Zheng

Guan

et al. 2020

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Background: The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms.Methods: The microarray data set of GSE66676 obtained from patients with hyperlipidaemia was downloaded. The weighted gene co‑expression network (WGCNA) analysis was used to analyze the gene expression profile and royalblue module was considered as 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 royalblue 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.Results: The significant module (royalblue) identified was associated with TC, TG and Non-HDL-C. GO and KEGG enrichment analyses revealed that the genes in the royalblue module were associated with carbon metabolism, steroid biosynthesis, fatty acid metabolism and biosynthesis of unsaturated fatty acids pathways. SQLE (degree = 17) was revealed as key molecules that associated with hypercholesterolemia (HCH) and SCD was revealed as key molecules that associated with hypertriglyceridemia (HTG). Meanwhile, RT-qPCR analysis also confirmed the above results based on our HCH/HTG samples.Conclusions: SQLE and SCD are related to hyperlipidaemia, SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.

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“…This step was done using the Bioconductor package in R software. [11]. If the expression value of multiple probe information corresponded to a gene with the same name, the average value was selected as the expression level of that gene.…”

Section: Methodsmentioning

confidence: 99%

Integrated weighted gene co-expression network analysis identified that TLR2 and CD40 are related to coronary artery disease

Qi¹,

Chen²,

Lin³

et al. 2020

Preprint

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Background: The current research attempted to identify possible hub genes and pathways of coronary artery disease (CAD) and to detect the possible mechanisms.Methods: 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, Disease Ontology 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 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.Results: 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. Approximately 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 were 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.Conclusion: 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.

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Weighted Gene Co-Expression Network Analysis Identifies Specific Modules and Hub Genes Related to Hyperlipidemia

Cited by 35 publications

References 47 publications

LncRNA SNHG8 is identified as a key regulator of acute myocardial infarction by RNA-seq analysis

LncRNA SNHG8 is identified as a key regulator of acute myocardial infarction by RNA-seq analysis

Weighted gene co-expression network analysis to identify key modules and hub genes related to hyperlipidaemia

Integrated weighted gene co-expression network analysis identified that TLR2 and CD40 are related to coronary artery disease

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