scholarly journals Transmembrane signaling molecules play a key role in the pathogenesis of IgA nephropathy: a weighted gene co-expression network analysis study

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Alieh Gholaminejad ◽  
Amir Roointan ◽  
Yousof Gheisari
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Immunoglobulin A ◽  
Cell Communication ◽  
Signaling Molecules ◽  
Health Concern ◽  
Transmembrane Signaling ◽  
Ppi Network ◽  
Hub Genes ◽  
Efficient Treatment

Abstract Background Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using the weighted gene co-expression network analysis (WGCNA) algorithm. Results GSE104948 dataset (the expression data from glomerular tissue of IgAN patients) was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease’s most correlated module were mainly enriched in the immune system, cell–cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potentially key players in the pathogenesis of IgAN. In the constructed regulatory network, hsa-miR-129-2-3p, hsa-miR-34a-5p and hsa-miR-27a-3p, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes. Conclusions The excavated hub genes from the hearts of co-expressed modules and the PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

scholarly journals Transmembrane Signaling Molecules Play a Key Role in Progression of IgA Nephropathy: A Weighted Gene Co-expression Network Analysis Study

2021 ◽  
Author(s):  
Alieh Gholaminejad ◽  
Amir Roointan ◽  
Yousof Gheisari
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Immunoglobulin A ◽  
Cell Communication ◽  
Signaling Molecules ◽  
Health Concern ◽  
Transmembrane Signaling ◽  
Ppi Network ◽  
Hub Genes ◽  
Efficient Treatment

Abstract Background: Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though, still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using weighted gene co-expression network analysis (WGCNA) algorithm. Results: GSE104948 dataset was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease most correlated module were mainly enriched in the immune system, cell-cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potential key players in the IgAN progression. In the constructed regulatory network, miR-129-2, miR-34a and miR-27a, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes.Conclusions: The excavated hub genes from the hearts of co-expressed modules and PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

scholarly journals Transmembrane Signaling Molecules Play a Key Role in Progression of IgA Nephropathy: A Weighted Gene Co-expression Network Analysis Study

2021 ◽  
Author(s):  
Alieh Gholaminejad ◽  
Amir roointan ◽  
Yousof Gheisari
Keyword(s):  
Network Analysis ◽  
Molecular Mechanisms ◽  
Immunoglobulin A ◽  
Cell Communication ◽  
Signaling Molecules ◽  
Health Concern ◽  
Transmembrane Signaling ◽  
Ppi Network ◽  
Hub Genes ◽  
Efficient Treatment

Abstract Background: Immunoglobulin A nephropathy (IgAN) is one of the most common primary glomerulonephritis and a serious health concern worldwide; though, still the underlying molecular mechanisms of IgAN are yet to be known and there is no efficient treatment for this disease. The main goal of this study was to explore the IgAN underlying pathogenic pathways, plus identifying the disease correlated modules and genes using weighted gene co-expression network analysis (WGCNA) algorithm. Results: GSE104948 dataset was analyzed and the identified differentially expressed genes (DEGs) were introduced to the WGCNA algorithm for building co-expression modules. Genes were classified into six co-expression modules. Genes of the disease most correlated module were mainly enriched in the immune system, cell-cell communication and transmembrane cell signaling pathways. The PPI network was constructed by genes in all the modules and after hub-gene identification and validation steps, 11 genes, mostly transmembrane proteins (CD44, TLR1, TLR2, GNG11, CSF1R, TYROBP, ITGB2, PECAM1), as well as DNMT1, CYBB and PSMB9 were identified as potential key players in the IgAN progression. In the constructed regulatory network, miR-129-2, miR-34a and miR-27a, as well as STAT3 were spotted as top molecules orchestrating the regulation of the hub genes.Conclusions: The excavated hub genes from the hearts of co-expressed modules and PPI network were mostly transmembrane signaling molecules. These genes and their upstream regulators could deepen our understanding of IgAN and be considered as potential targets for hindering its progression.

scholarly journals Weighted gene co-expression network analysis reveals specific modules and hub genes related to neuropathic pain in dorsal root ganglions

2019 ◽  
Vol 39 (11) ◽  
Author(s):  
Nan Cheng ◽  
Zheng Zhang ◽  
Yue Guo ◽  
Zhuo-Lin Qiu ◽  
Jing-Yi Du ◽  
...  
Keyword(s):  
Neuropathic Pain ◽  
Network Analysis ◽  
Dorsal Root ◽  
Molecular Mechanisms ◽  
Tissue Inhibitor Of Metalloproteinase ◽  
In Vivo Studies ◽  
Ppi Network ◽  
Hub Genes ◽  
Clinical Issue

Abstract Neuropathic pain is a common, debilitating clinical issue. Here, the weighted gene co-expression network analysis (WGCNA) was used to identify the specific modules and hub genes that are related to neuropathic pain. The microarray dataset of a neuropathic rat model induced by tibial nerve transection (TNT), including dorsal root ganglion (DRG) tissues from TNT model (n=7) and sham (n=8) rats, was downloaded from the ArrayExpress database (E-MTAB-2260). The co-expression network modules were identified by the WGCNA package. The protein–protein interaction (PPI) network was constructed, and the node with highest level of connectivity in the network were identified as the hub gene. A total of 1739 genes and seven modules were identified. The most significant module was the brown module, which contained 215 genes that were primarily associated with the biological process (BP) of the defense response and molecular function of calcium ion binding. Furthermore, C–C motif chemokine ligand 2 (Ccl2), Fos and tissue inhibitor of metalloproteinase 1 (Timp1) which were identified as the hub genes in the PPI network and two subnetworks separately. The in vivo studies validated that mRNA and protein levels of Ccl2, Fos and Timp1 were up-regulated in DRG and spinal cord tissues after TNT. The present study offers novel insights into the molecular mechanisms of neuropathic pain in the context of peripheral nerve injury.

scholarly journals Identification of Novel Hub Genes Through Expression Profiles Analysis in Adrenocortical Carcinoma

2020 ◽  
Author(s):  
Chenhe Yao ◽  
Xiaoling Zhao ◽  
Xuemeng Shang ◽  
Binghan Jia ◽  
Shuaijie Dou ◽  
...  
Keyword(s):  
Adrenocortical Carcinoma ◽  
Expression Profiling ◽  
Molecular Mechanisms ◽  
Cross Validation ◽  
Expression Profiles ◽  
Ppi Network ◽  
Hub Genes ◽  
Tumor Association ◽  
Prediction Of Prognosis ◽  
Geo Database

Abstract Background: Adrenocortical carcinoma (ACC) is a heterogeneous and rare malignant tumor associated with a poor prognosis. The molecular mechanisms of ACC remain elusive and more accurate biomarkers for the prediction of prognosis are needed.Methods: In this study, integrative profiling analyses were performed to identify novel hub genes in ACC to provide promising targets for future investigation. Three gene expression profiling datasets in the GEO database were used for the identification of overlapped differentially expressed genes (DEGs) following the criteria of adj.P.Value<0.05 and |log2 FC|>0.5 in ACC. Novel hub genes were screened out following a series of processes: the retrieval of DEGs with no known associations with ACC on Pubmed, then the cross-validation of expression values and significant associations with overall survival in the GEPIA2 and starBase databases, and finally the prediction of gene-tumor association in the GeneCards database.Results: Four novel hub genes were identified and two of them, TPX2 and RACGAP1, were positively correlated with the staging. Interestingly, co-expression analysis revealed that the association between TPX2 and RACGAP1 was the strongest and that the expression of HOXA5 was almost completely independent of that of RACGAP1 and TPX2. Furthermore, the PPI network consisting of four novel genes and seed genes in ACC revealed that HOXA5, TPX2, and RACGAP1 were all associated with TP53. Conclusions: This study identified four novel hub genes (TPX2, RACHAP1, HXOA5 and FMO2) that may play crucial roles in the tumorigenesis and the prediction of prognosis of ACC.

Identification of Four Key Biomarkers and Small Molecule Drugs Innasopharyngeal Carcinoma by Weighted Gene Co-expression Network Analysis

2020 ◽  
Author(s):  
Xi Pan ◽  
Jian-Hao Liu
Keyword(s):  
Gene Expression ◽  
Network Analysis ◽  
Small Molecule ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Tumor Stage ◽  
Functional Enrichment ◽  
Hub Genes ◽  
Therapeutic Goals ◽  
Small Molecule Drugs

Abstract Background Nasopharyngeal carcinoma (NPC) is a heterogeneous carcinoma that the underlying molecular mechanisms involved in the tumor initiation, progression, and migration are largely unclear. The purpose of the present study was to identify key biomarkers and small-molecule drugs for NPC screening, diagnosis, and therapy via gene expression profile analysis. Methods Raw microarray data of NPC were retrieved from the Gene Expression Omnibus (GEO) database and analyzed to screen out the potential differentially expressed genes (DEGs). The key modules associated with histology grade and tumor stage was identified by using weighted correlation network analysis (WGCNA). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of genes in the key module were performed to identify potential mechanisms. Candidate hub genes were obtained, which based on the criteria of module membership (MM) and high connectivity. Then we used receiver operating characteristic (ROC) curve to evaluate the diagnostic value of hub genes. The Connectivity map database was further used to screen out small-molecule drugs of hub genes. Results A total of 430 DEGs were identified based on two GEO datasets. The green gene module was considered as key module for the tumor stage of NPC via WGCNA analysis. The results of functional enrichment analysis revealed that genes in the green module were enriched in regulation of cell cycle, p53 signaling pathway, cell part morphogenesis. Furthermore, four DEGs-related hub genes in the green module were considered as the final hub genes. Then ROC revealed that the final four hub genes presented with high areas under the curve, suggesting these hub genes may be diagnostic biomarkers for NPC. Meanwhile, we screened out several small-molecule drugs that have provided potentially therapeutic goals for NPC. Conclusions Our research identified four potential prognostic biomarkers and several candidate small-molecule drugs for NPC, which may contribute to the new insights for NPC therapy.

scholarly journals Gene Differential Expression and Interaction Networks Illustrate the Biomarkers and Molecular Mechanisms of Atherosclerotic Cerebral Infarction

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Benzhuo Zhang ◽  
Wei Huang ◽  
Mingquan Yi ◽  
Chunxu Xing
Keyword(s):  
Network Analysis ◽  
Cerebral Infarction ◽  
Antigen Processing ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Neutrophil Activation ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Geo Database

Atherosclerotic cerebral infarction (ACI) seriously threatens the health of the senile patients, and the strategies are urgent for the diagnosis and treatment of ACI. This study investigated the mRNA profiling of the patients with ischemic stroke and atherosclerosis via excavating the datasets in the GEO database and attempted to reveal the biomarkers and molecular mechanism of ACI. In this study, GES16561 and GES100927 were obtained from Gene Expression Omnibus (GEO) database, and the related differentially expressed genes (DEGs) were analyzed with R language. Furthermore, the DEGs were analyzed with Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Besides, the protein-protein interaction (PPI) network of DEGs was analyzed by STRING database and Cytoscape. The results showed that 133 downregulated DEGs and 234 upregulated DEGs were found in GES16561, 25 downregulated DEGs and 104 upregulated DEGs were found in GSE100927, and 6 common genes were found in GES16561 and GES100927. GO enrichment analysis showed that the functional models of the common genes were involved in neutrophil activation, neutrophil degranulation, neutrophil activation, and immune response. KEGG enrichment analysis showed that the DEGs in both GSE100927 and GSE16561 were connected with the pathways including Cell adhesion molecules (CAMs), Cytokine-cytokine receptor interaction, Phagosome, Antigen processing and presentation, and Staphylococcus aureus infection. The PPI network analysis showed that 9 common DEGs were found in GSE100927 and GSE16561, and a cluster with 6 nodes and 12 edges was also identified by PPI network analysis. In conclusion, this study suggested that FCGR3A and MAPK pathways were connected with ACI.

scholarly journals Identification of potential key genes and pathway linked with sporadic Creutzfeldt-Jakob disease based on integrated bioinformatics analyses

2020 ◽  
Author(s):  
Basavaraj Vastrad ◽  
Chanabasayya Vastrad ◽  
Iranna Kotturshetti
Keyword(s):  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Target Genes ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Bioinformatics Analyses ◽  
Go Enrichment ◽  
Jakob Disease

AbstractSporadic Creutzfeldt-Jakob disease (sCJD) is neurodegenerative disease also called prion disease linked with poor prognosis. The aim of the current study was to illuminate the underlying molecular mechanisms of sCJD. The mRNA microarray dataset GSE124571 was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened. Pathway and GO enrichment analyses of DEGs were performed. Furthermore, the protein-protein interaction (PPI) network was predicted using the IntAct Molecular Interaction Database and visualized with Cytoscape software. In addition, hub genes and important modules were selected based on the network. Finally, we constructed target genes - miRNA regulatory network and target genes - TF regulatory network. Hub genes were validated. A total of 891 DEGs 448 of these DEGs presented significant up regulated, and the remaining 443 down regulated were obtained. Pathway enrichment analysis indicated that up regulated genes were mainly linked with glutamine degradation/glutamate biosynthesis, while the down regulated genes were involved in melatonin degradation. GO enrichment analyses indicated that up regulated genes were mainly linked with chemical synaptic transmission, while the down regulated genes were involved in regulation of immune system process. hub and target genes were selected from the PPI network, modules, and target genes - miRNA regulatory network and target genes - TF regulatory network namely YWHAZ, GABARAPL1, EZR, CEBPA, HSPB8, TUBB2A and CDK14. The current study sheds light on the molecular mechanisms of sCJD and may provide molecular targets and diagnostic biomarkers for sCJD.

scholarly journals Analysis of potential genetic biomarkers and molecular mechanism of smoking-related postmenopausal osteoporosis using weighted gene co-expression network analysis and machine learning

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0257343
Author(s):  
Shaoshuo Li ◽  
Baixing Chen ◽  
Hao Chen ◽  
Zhen Hua ◽  
Yang Shao ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
Network Analysis ◽  
Postmenopausal Osteoporosis ◽  
Molecular Mechanism ◽  
Molecular Mechanisms ◽  
Functional Enrichment ◽  
Support Vector ◽  
Hub Genes ◽  
Genetic Biomarkers

Objectives Smoking is a significant independent risk factor for postmenopausal osteoporosis, leading to genome variations in postmenopausal smokers. This study investigates potential biomarkers and molecular mechanisms of smoking-related postmenopausal osteoporosis (SRPO). Materials and methods The GSE13850 microarray dataset was downloaded from Gene Expression Omnibus (GEO). Gene modules associated with SRPO were identified using weighted gene co-expression network analysis (WGCNA), protein-protein interaction (PPI) analysis, and pathway and functional enrichment analyses. Feature genes were selected using two machine learning methods: support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF). The diagnostic efficiency of the selected genes was assessed by gene expression analysis and receiver operating characteristic curve. Results Eight highly conserved modules were detected in the WGCNA network, and the genes in the module that was strongly correlated with SRPO were used for constructing the PPI network. A total of 113 hub genes were identified in the core network using topological network analysis. Enrichment analysis results showed that hub genes were closely associated with the regulation of RNA transcription and translation, ATPase activity, and immune-related signaling. Six genes (HNRNPC, PFDN2, PSMC5, RPS16, TCEB2, and UBE2V2) were selected as genetic biomarkers for SRPO by integrating the feature selection of SVM-RFE and RF. Conclusion The present study identified potential genetic biomarkers and provided a novel insight into the underlying molecular mechanism of SRPO.

scholarly journals Identification of miRNA Regulatory Networks and Candidate Markers for Fracture Healing in Mice

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Xianglu Li ◽  
Zhaohua Zhong ◽  
Enguang Ma ◽  
Xiaowei Wu
Keyword(s):  
Network Analysis ◽  
Fracture Healing ◽  
Collagen Fibril ◽  
Regulatory Networks ◽  
Expression Profiles ◽  
Healing Process ◽  
Differentially Expressed ◽  
Differential Analysis ◽  
Ppi Network ◽  
Hub Genes

Background. It is important to improve the understanding of the fracture healing process at the molecular levels, then to discover potential miRNA regulatory mechanisms and candidate markers. Methods. Expression profiles of mRNA and miRNA were obtained from the Gene Expression Omnibus database. We performed differential analysis, enrichment analysis, protein-protein interaction (PPI) network analysis. The miRNA-mRNA network analysis was also performed. Results. We identified 499 differentially expressed mRNAs (DEmRs) that were upregulated and 534 downregulated DEmRs during fracture healing. They were mainly enriched in collagen fibril organization and immune response. Using the PPI network, we screened 10 hub genes that were upregulated and 10 hub genes downregulated with the largest connectivity. We further constructed the miRNA regulatory network for hub genes and identified 13 differentially expressed miRNAs (DEmiRs) regulators. Cd19 and Col6a1 were identified as key candidate mRNAs with the largest fold change, and their DEmiR regulators were key candidate regulators. Conclusion. Cd19 and Col6a1 might serve as candidate markers for fracture healing in subsequent studies. Their expression is regulated by miRNAs and is involved in collagen fibril organization and immune responses.

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