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

2020 ◽  
Author(s):  
Fu Jun Liao ◽  
Peng-Fei Zheng ◽  
Yao-Zong Guan ◽  
Wei Li
Keyword(s):  
Molecular Mechanisms ◽  
Acid Metabolism ◽  
Unsaturated Fatty Acids ◽  
Kegg Pathway ◽  
Hub Genes ◽  
Data Set ◽  
Online Tool ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Qpcr Analysis

Abstract 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.

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

2021 ◽  
Author(s):  
Fu Jun Liao ◽  
Peng-Fei Zheng ◽  
Yao-Zong Guan ◽  
Hong Wei Pan ◽  
Wei Li
Keyword(s):  
Molecular Mechanisms ◽  
Acid Metabolism ◽  
Unsaturated Fatty Acids ◽  
Microarray Dataset ◽  
Hub Genes ◽  
Online Tool ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Qpcr Analysis ◽  
Blue Module

Abstract Background: The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms.Methods: The 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.Results: The 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.Conclusions: SQLE and SCD are related to hyperlipidaemia, and SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.

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

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Fu-Jun Liao ◽  
Peng-Fei Zheng ◽  
Yao-Zong Guan ◽  
Hong-Wei Pan ◽  
Wei Li
Keyword(s):  
Molecular Mechanisms ◽  
Acid Metabolism ◽  
Unsaturated Fatty Acids ◽  
Microarray Dataset ◽  
Hub Genes ◽  
Online Tool ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Qpcr Analysis ◽  
Blue Module

Abstract Background The purpose of this study was to explore the potential molecular targets of hyperlipidaemia and the related molecular mechanisms. Methods The 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. Results The 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. Conclusions SQLE and SCD are related to hyperlipidaemia, and SQLE/SCD may be new targets for cholesterol-lowering or triglyceride-lowering therapy, respectively.

scholarly journals Construction of a Novel Ferroptosis-Related Gene Signature of Atherosclerosis

2022 ◽  
Vol 9 ◽  
Author(s):  
Tucheng Huang ◽  
Kangjie Wang ◽  
Yuewei Li ◽  
Yanchen Ye ◽  
Yangxin Chen ◽  
...  
Keyword(s):  
Regulatory Mechanism ◽  
Kegg Pathway ◽  
Gene Signature ◽  
Chronic Inflammatory Disease ◽  
Ppi Network ◽  
Competing Endogenous Rna ◽  
Hub Genes ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Causes Of Mortality

Atheroclerosis refers to a chronic inflammatory disease featured by the accumulation of fibrofatty lesions in the intima of arteries. Cardiovasular events associated with atherosclerosis remain the major causes of mortality worldwide. Recent studies have indicated that ferroptosis, a novel programmed cell death, might participate in the process of atherosclerosis. However, the ferroptosis landscape is still not clear. In this study, 59 genes associated with ferroptosis were ultimately identified in atherosclerosis in the intima. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed for functional annotation. Through the construction of protein–protein interaction (PPI) network, five hub genes (TP53, MAPK1, STAT3, HMOX1, and PTGS2) were then validated histologically. The competing endogenous RNA (ceRNA) network of hub genes was ultimately constructed to explore the regulatory mechanism between lncRNAs, miRNAs, and hub genes. The findings provide more insights into the ferroptosis landscape and, potentially, the therapeutic targets of atherosclerosis.

scholarly journals Exploring The Molecular Mechanisms of Classical Hodgkin Lymphoma Through Bioinformatics Analysis

2021 ◽  
Author(s):  
Zhu Lili ◽  
Zhu YuKun ◽  
Zhuangzhuang Tian ◽  
Yongsheng Li ◽  
Liyu Cao
Keyword(s):  
Hodgkin Lymphoma ◽  
Protein Interaction ◽  
Protein Interaction Network ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Protein Protein Interaction Network

Abstract Background Classic Hodgkin lymphoma (CHL) is the most common HL in the modern society. Although the treatment of cHL has made great progress, its molecular mechanisms have yet to be deciphered. Objectives The purpose of this study is to find out the crucial potential genes and pathways associated with cHL. Methods We downloaded the cHL microarray dataset (GSE12453) from Gene Expression Omnibus (GEO) database and to identify the differentially expressed genes (DEGs) between cHL samples and normal samples through the limma package in R. Then, gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were carried out. Finally, we constructed the protein-protein interaction network to screen out the hub genes using Search Tool for the Retrieval of Interacting Genes (STRING) database. Results We screened out 788 DEGs in the cHL dataset, such as BATF3, IER3, RAB13 and FCRL2. GO functional enrichment analysis indicated that the DEGs were related with regulation of lymphocyte activation, secretory granule lumen and chemokine activity. KEGG pathway analysis showed that the genes enriched in Prion disease, Complement and coagulation cascades and Parkinson disease Coronavirus disease-COVID-19 pathway. Protein-protein interaction network construction identified 10 hub genes (IL6, ITGAM, CD86, FN1, MMP9, CXCL10, CCL5, CD19, IFNG, SELL, UBB) in the network. Conclusions In the present investigation, we identified several pathways and hub genes related to the occurrence and development of cHL, which may provide an important basis for further research and novel therapeutic targets and prognostic indicators for cHL.

scholarly journals An integrative bioinformatics analysis for identifying hub genes associated with infection of lung samples in patients infected with SARS-CoV-2

2021 ◽  
Vol 26 (1) ◽  
Author(s):  
Tian-Ao Xie ◽  
Zhi-Jian He ◽  
Chuan Liang ◽  
Hao-Neng Dong ◽  
Jie Zhou ◽  
...  
Keyword(s):  
World Health ◽  
Pathophysiological Mechanism ◽  
Ppi Network ◽  
Hub Genes ◽  
Data Set ◽  
Online Tool ◽  
Protein Protein Interaction ◽  
The World ◽  
Health Organization ◽  
Gene Data

Abstract Background At the end of 2019, the world witnessed the emergence and ravages of a viral infection induced by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Also known as the coronavirus disease 2019 (COVID-19), it has been identified as a public health emergency of international concern (PHEIC) by the World Health Organization (WHO) because of its severity. Methods The gene data of 51 samples were extracted from the GSE150316 and GSE147507 data set and then processed by means of the programming language R, through which the differentially expressed genes (DEGs) that meet the standards were screened. The Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed on the selected DEGs to understand the functions and approaches of DEGs. The online tool STRING was employed to construct a protein–protein interaction (PPI) network of DEGs and, in turn, to identify hub genes. Results A total of 52 intersection genes were obtained through DEG identification. Through the GO analysis, we realized that the biological processes (BPs) that have the deepest impact on the human body after SARS-CoV-2 infection are various immune responses. By using STRING to construct a PPI network, 10 hub genes were identified, including IFIH1, DDX58, ISG15, EGR1, OASL, SAMD9, SAMD9L, XAF1, IFITM1, and TNFSF10. Conclusion The results of this study will hopefully provide guidance for future studies on the pathophysiological mechanism of SARS-CoV-2 infection.

scholarly journals Cell Adhesion-Related Molecules Play a Key Role in Renal Cancer Progression by Multinetwork Analysis

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Anbang Wang ◽  
Ming Chen ◽  
Hui Wang ◽  
Jinming Huang ◽  
Yi Bao ◽  
...  
Keyword(s):  
Cell Adhesion ◽  
Cancer Progression ◽  
Kegg Pathway ◽  
Ppi Network ◽  
Hub Genes ◽  
Genetic Characteristics ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Underlying Mechanisms ◽  
Upregulated Genes

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system. The study aimed to identify genetic characteristics and reveal the underlying mechanisms in RCC. GSE53757, GSE46699, and TCGA KIRC database (n = 897) were analyzed to screen differentially expressed genes (DEGs) in RCC. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, followed by the analysis of the protein-protein interaction (PPI) network of the DEGs by Cytoscape software. In all, 834 DEGs were identified in RCC, including 416 upregulated genes and 418 downregulated genes. The top 10 hub genes, VEGFA, EGFR, EGF, CD44, CD86, FN1, ITGAM, ITGB2, TLR2, and PTPRC, were identified from the PPI network according to the core degree. The following subnetwork revealed that these significant modules were enriched in positive regulation of response to external stimulus, regulation of leukocyte-mediated immunity, and regulation of exocytosis. The expressions of these hub genes were also validated using qRT-PCR and IHC in Changzheng RCC database (n = 160). We especially found that half of the top ten hub genes were cell adhesion-related molecules, which were associated with RCC progression and poor prognosis. In conclusion, these hub genes, particularly cell adhesion-related molecules, could be used as prognostic biomarkers and potential therapeutic targets for RCC.

scholarly journals POS0742 SCREENING AND BIOINFORMATICS ANALYSIS OF HUB GENES AND PATHWAYS FOR PRIMARY SJÖGREN’S SYNDROME BASED ON GEO DATABASE

2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 622.2-623
Author(s):  
T. Kong ◽  
S. X. Zhang ◽  
S. Song ◽  
X. Sun ◽  
C. Zheng ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Sjogren's Syndrome ◽  
Primary Sjögren’S Syndrome ◽  
Primary Sjogren’S Syndrome ◽  
Hub Genes ◽  
Primary Sjögren's Syndrome ◽  
Pathway Enrichment ◽  
Geo Database

Background:Primary Sjögren’s syndrome (pSS) is an autoimmune disease that featured as lymphoplasmacytic infiltration of the exocrine glands leading to sicca symptoms1. However, its underlying molecular mechanisms remain elusive.Objectives:This study aims to identify differentially expressed genes (DEGs) and pathways associated with the progression of pSS using bioinformatics analysis and explore its pathogenesis.Methods:The pSS-associated gene chip data set GSE66795 was obtained from the Gene Expression Omnibus (GEO) database, which included 131 cases of fully-phenotyped pSS patients’ whole blood samples and 29 cases of control samples. DEGs were screened Using R software. Online tool Metascape2 was used to make Gene Ontology (GO) and KEGG pathway enrichment. The PPI network was performed using String database. Hub genes were identified by Cytoscape.Results:A total of 108 DEGs were captured, including 101 up-regulated genes and 7 down-regulated genes. GO enrichment showed that these DEGs were primarily enriched in defense response to virus, response to interferon-gamma, regulation of innate immune response, response to interferon-beta, double-stranded RNA binding, response to interferon-alpha. KEGG pathway enrichment analysis showed these DEGs were principally enriched in Influenza A, RIG-I-like receptor signaling pathway, necroptosis, Staphylococcus aureus infection. Finally, 9 hub genes (STAT1, IRF7, OAS2, GBP1, OAS1, IFIT3, IFIH1, OAS3, DDX60) had highest degree value.Conclusion:The findings identified molecular mechanisms and the key hub genes that may involve in the occurrence and development of pSS.References:[1]Francois H, Mariette X. Renal involvement in primary Sjogren syndrome. Nat Rev Nephrol 2016;12(2):82-93. doi: 10.1038/nrneph.2015.174 [published Online First: 2015/11/17].[2]Zhou Y, Zhou B, Pache L, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat Commun 2019;10(1):1523. doi: 10.1038/s41467-019-09234-6 [published Online First: 2019/04/05].Acknowledgements:This project was supported by National Science Foundation of China (82001740), Open Fund from the Key Laboratory of Cellular Physiology (Shanxi Medical University) (KLCP2019) and Innovation Plan for Postgraduate Education in Shanxi Province (2020BY078).Disclosure of Interests:None declared

scholarly journals Identification of the Molecular Mechanisms of Peimine in the Treatment of Cough Using Computational Target Fishing

Molecules ◽  
2020 ◽  
Vol 25 (5) ◽  
pp. 1105 ◽  
Author(s):  
Lihua Zhang ◽  
Mingchao Cui ◽  
Shaojun Chen
Keyword(s):  
Drug Targets ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Interaction Network ◽  
Biological Processes ◽  
Protein Targets ◽  
Functional Annotations ◽  
Pathway Enrichment ◽  
Protein Protein Interaction ◽  
Target Fishing

Peimine (also known as verticine) is the major bioactive and characterized compound of Fritillariae Thunbergii Bulbus, a traditional Chinese medicine that is most frequently used to relieve a cough. Nevertheless, its molecular targets and mechanisms of action for cough are still not clear. In the present study, potential targets of peimine for cough were identified using computational target fishing combined with manual database mining. In addition, protein-protein interaction (PPI), gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed using, GeneMANIA and Database for Annotation, Visualization and Integrated Discovery (DAVID) databases respectively. Finally, an interaction network of drug-targets-pathways was constructed using Cytoscape. The results identified 23 potential targets of peimine associated with cough, and suggested that MAPK1, AKT1 and PPKCB may be important targets of pemine for the treatment of cough. The functional annotations of protein targets were related to the regulation of immunological and neurological function through specific biological processes and related pathways. A visual representation of the multiple targets and pathways that form a network underlying the systematic actions of peimine was generated. In summary, peimine is predicted to exert its systemic pharmacological effects on cough by targeting a network composed of multiple proteins and pathways.

scholarly journals Screen Key Genes Associated with Distraction-Induced Osteogenesis of Stem Cells Using Bioinformatics Methods

2021 ◽  
Vol 22 (12) ◽  
pp. 6505
Author(s):  
Jishizhan Chen ◽  
Jia Hua ◽  
Wenhui Song
Keyword(s):  
Stem Cells ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Negative Control ◽  
Gene Set Enrichment Analysis ◽  
Venn Diagram ◽  
Hub Genes ◽  
Protein Protein Interaction ◽  
Two Samples ◽  
Key Genes

Applying mesenchymal stem cells (MSCs), together with the distraction osteogenesis (DO) process, displayed enhanced bone quality and shorter treatment periods. The DO guides the differentiation of MSCs by providing mechanical clues. However, the underlying key genes and pathways are largely unknown. The aim of this study was to screen and identify hub genes involved in distraction-induced osteogenesis of MSCs and potential molecular mechanisms. Material and Methods: The datasets were downloaded from the ArrayExpress database. Three samples of negative control and two samples subjected to 5% cyclic sinusoidal distraction at 0.25 Hz for 6 h were selected for screening differentially expressed genes (DEGs) and then analysed via bioinformatics methods. The Gene Ontology (GO) terms and Kyoto Encyclopaedia of Genes and Genomes (KEGG) pathway enrichment were investigated. The protein–protein interaction (PPI) network was visualised through the Cytoscape software. Gene set enrichment analysis (GSEA) was conducted to verify the enrichment of a self-defined osteogenic gene sets collection and identify osteogenic hub genes. Results: Three hub genes (IL6, MMP2, and EP300) that were highly associated with distraction-induced osteogenesis of MSCs were identified via the Venn diagram. These hub genes could provide a new understanding of distraction-induced osteogenic differentiation of MSCs and serve as potential gene targets for optimising DO via targeted therapies.

scholarly journals Bioinformatics analysis of differentially expressed genes and identification of an miRNA–mRNA network associated with entorhinal cortex and hippocampus in Alzheimer’s disease

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Haoming Li ◽  
Linqing Zou ◽  
Jinhong Shi ◽  
Xiao Han
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differentially Expressed Genes ◽  
Entorhinal Cortex ◽  
Molecular Mechanisms ◽  
Kegg Pathway ◽  
Neurodegenerative Disorder ◽  
Early Stage ◽  
Differentially Expressed ◽  
Hub Genes

Abstract Background Alzheimer’s disease (AD) is a fatal neurodegenerative disorder, and the lesions originate in the entorhinal cortex (EC) and hippocampus (HIP) at the early stage of AD progression. Gaining insight into the molecular mechanisms underlying AD is critical for the diagnosis and treatment of this disorder. Recent discoveries have uncovered the essential roles of microRNAs (miRNAs) in aging and have identified the potential of miRNAs serving as biomarkers in AD diagnosis. Methods We sought to apply bioinformatics tools to investigate microarray profiles and characterize differentially expressed genes (DEGs) in both EC and HIP and identify specific candidate genes and pathways that might be implicated in AD for further analysis. Furthermore, we considered that DEGs might be dysregulated by miRNAs. Therefore, we investigated patients with AD and healthy controls by studying the gene profiling of their brain and blood samples to identify AD-related DEGs, differentially expressed miRNAs (DEmiRNAs), along with gene ontology (GO) analysis, KEGG pathway analysis, and construction of an AD-specific miRNA–mRNA interaction network. Results Our analysis identified 10 key hub genes in the EC and HIP of patients with AD, and these hub genes were focused on energy metabolism, suggesting that metabolic dyshomeostasis contributed to the progression of the early AD pathology. Moreover, after the construction of an miRNA–mRNA network, we identified 9 blood-related DEmiRNAs, which regulated 10 target genes in the KEGG pathway. Conclusions Our findings indicated these DEmiRNAs having the potential to act as diagnostic biomarkers at an early stage of AD.

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