scholarly journals Identification of the Glucose Metabolism Biomarkers of NASH: Weighted Gene Co-Expression Network Analysis

2022 ◽  
Author(s):  
Xingyuan Chen ◽  
Zhanhui Ye ◽  
Kequan Chen ◽  
Jiahui Xu ◽  
Liangying Ye ◽  
...  
Keyword(s):  
Network Analysis ◽  
Glucose Metabolism ◽  
Cell Model ◽  
Nile Red ◽  
Ring Finger ◽  
Glucose Consumption ◽  
Gene Expression Omnibus ◽  
Regulatory Subunit ◽  
Control Group ◽  
Hub Genes

Abstract Background The genetic mechanism of glucose metabolism has not been elucidated in nonalcoholic steatohepatitis (NASH), and many genes are took part in glucose metabolism of NASH. In this study, we used the weighted gene co-expression network analysis (WGCNA) to find the key genes associated with glucose metabolism; Methods Data sets GSE96971 and GSE89632 from Gene Expression Omnibus (GEO) were analyzed by WGCNA. We screened the Hub gene from the GSE96971 dataset, and the selected Hub genes were verified by GSE89632 dataset. We then analyzed the dataset using the Gene Ontology (GO) term enrichment and the Kyoto Encyclopedia of Genome (KEGG) path analysis. Expression levels of the hub genes are assessed by qPCR analysis. The function of hub genes was verified by Nile Red staining and relative glucose consumption detection; Results The hub genes are mannosidase beta like (MANBAL), myc proto-oncogene protein (MYC), caspase 4 (CASP4), CDK5 regulatory subunit associated protein 3 (CDK5RAP3) and ZFP36 ring finger protein (ZFP36) in the datasets GSE96971 and the GSE89632. Further, these genes are mainly involved in the integral component of membrane and plasma membrane, the PI3K-AKT signaling pathway and the olfactory transduction according to the GO and KEGG results. These hub genes were significantly up-regulated in the palmitic acid (PA) cell model and methionine-choline-deficient medium (MCD) cell model. After knocking out the hub genes in PA model and the MCD model of NASH, relative glucose consumption was increased and lipid deposition was reduced compared with the control group; Conclusions MANBAL, MYC, CASP4, CDK5RAP3 and ZFP36 are elevated and involved in the pathogenesis of NASH. Further research on these genes are warranted.

Potential Biomarkers of Acute Myocardial Infarction Based on Co-Expression Network Analysis

2020 ◽  
Author(s):  
Zhiwen Ding ◽  
Zhaohui Hu ◽  
Xiangjun Ding ◽  
Yuyao Ji ◽  
Guiyuan li ◽  
...  
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Network Analysis ◽  
Pearson Correlation ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Control Group ◽  
Hub Genes ◽  
And Control ◽  
Potential Biomarkers

Abstract Background: Acute myocardial infarction (AMI) is a common cause of death in many countries. Analyzing the potential biomarkers of AMI is crucial to understanding the molecular mechanism of disease. However, specific diagnostic biomarkers have not been fully elucidated, and candidate regulatory targets for AMI have not been determined.Methods: In this study, AMI gene chip data GSE48060, blood samples from normal cardiac function controls (n = 21) and AMI patients (n = 26) were downloaded from Gene Expression Omnibus. The differentially expressed genes (DEGs) of AMI and control group were identified with Online tool GEO2R. the genes co-expressed with were found. The co-expression network of DEGs was analyzed by calculating the Pearson correlation coefficient of all gene pairs, MR screening and cutoff threshold screening. Then, GO database was used to analyze the function and pathway enrichment of genes in the most important modules. KEGG DISEASE and BioCyc were used to analyze the hub gene in the module to determine important sub-pathways. In addition, the expression of hub genes were certified by RT-qPCR in AMI and control specimens.Results: This study identified 52 DEGs, including 26 up-regulated genes and 26 down-regulated genes. Co-expression network analysis of 52 DEGs revealed that there are mainly three up-regulated genes (AKR1C3, RPS24 and P2RY12) and three down-regulated genes (ACSL1, B3GNT5 and MGAM) as key hub genes in the co-expression network. Furthermore, GO enrichment analysis was performed on all AMI co-expression network genes and found to be functionally enriched mainly in RAGE receptor binding and negative regulation of T cell cytokine production. In addition, through KEGG DISEASE and BioCyc analysis, the functions of genes RPS24 and P2RY12 were enriched in cardiovascular diseases, AKR1C3 was enriched in cardenolide biosynthesis, MGAM was enriched in glycogenolysis, B3GNT5 was enriched in glycosphingolipids biosynthesis, and ACSL1 enriched in icosapentaenoate biosynthesis II. Conclusion: The hub genes AKR1C3, RPS24, P2RY12, ACSL1, B3GNT5 and MGAM are potential targets of AMI and have potential application value in the diagnosis of AMI.

Identification of hub genes in cervical cancer using weighted gene co-expression network analysis

2020 ◽  
Author(s):  
Xiao Feng Lv ◽  
Ruyue Gong ◽  
Xiao Han ◽  
Shihong Cui
Keyword(s):  
Cervical Cancer ◽  
Network Analysis ◽  
Gene Mutations ◽  
Cervical Squamous Cell Carcinoma ◽  
Gene Expression Omnibus ◽  
Cancer Prognosis ◽  
Hub Genes ◽  
Dna Mismatch ◽  
Incidence And Mortality ◽  
Core Genes

Abstract Background Cervical cancer ranks second among malignancies in females around the world. Due to the elevated incidence and mortality of this malignancy, deciphering its pathogenesis and identifying related biomarkers is urgently required. Methods First, raw cervical squamous cell carcinoma (CESC) data in GSE63514 were downloaded from the Gene Expression Omnibus (GEO) database. Then, weighted Correlation Network Analysis (WGCNA) was performed to build a co-expression network. Next, comprehensive bioinformatics was performed to determine hub genes, and assess the associated functional annotation, prognostic signature, tumor immunity, DNA mismatch repair, methylation mechanism, candidate molecular drugs, and gene mutations. Results From the key module, ALOX12B, KRT78, RHOD and ZNF750 were selected for validation. K-M plots indicated that these genes had good diagnostic and prognostic values in CESC. Moreover, mutations in these hub genes resulted in the downregulation of most immune genes in CESC. On the other hand, most of the four core genes were negatively correlated with DNA mismatch genes. In addition, we found that RHOD and ZNF750 had decreased methylation in the disease state, while ALOX12B and KRT78 showed no significant differences. Meanwhile, GSVA revealed that most core genes had associations with P53 signaling and the hypoxia signaling pathway. Conclusion WGCNA could identify groups of genes significantly associated with cervical cancer prognosis. These findings provide new insights into CESC pathogenesis, and identify ALOX12B, KRT78, RHOD and ZNF750 as candidate biomarkers for CESC diagnosis and prognosis.

scholarly journals A nomogram for predicting metabolic steatohepatitis: The combination of NAMPT, RALGDS, GADD45B, FOSL2, RTP3, and RASD1

Open Medicine ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. 773-785
Author(s):  
Shenling Liao ◽  
He He ◽  
Yuping Zeng ◽  
Lidan Yang ◽  
Zhi Liu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Fatty Liver ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Quantitative Detection ◽  
Control Group ◽  
Calibration Plot ◽  
Hub Genes ◽  
Threshold Probability ◽  
Clinically Significant

Abstract Objective To identify differentially expressed and clinically significant mRNAs and construct a potential prediction model for metabolic steatohepatitis (MASH). Method We downloaded four microarray datasets, GSE89632, GSE24807, GSE63067, and GSE48452, from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) analysis and weighted gene co-expression network analysis were performed to screen significant genes. Finally, we constructed a nomogram of six hub genes in predicting MASH and assessed it through receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). In addition, qRT-PCR was used for relative quantitative detection of RNA in QSG-7011 cells to further verify the expression of the selected mRNA in fatty liver cells. Results Based on common DEGs and brown and yellow modules, seven hub genes were identified, which were NAMPT, PHLDA1, RALGDS, GADD45B, FOSL2, RTP3, and RASD1. After logistic regression analysis, six hub genes were used to establish the nomogram, which were NAMPT, RALGDS, GADD45B, FOSL2, RTP3, and RASD1. The area under the ROC of the nomogram was 0.897. The DCA showed that when the threshold probability of MASH was 0–0.8, the prediction model was valuable to GSE48452. In QSG-7011 fatty liver model cells, the relative expression levels of NAMPT, GADD45B, FOSL2, RTP3, RASD1 and RALGDS were lower than the control group. Conclusion We identified seven hub genes NAMPT, PHLDA1, RALGDS, GADD45B, FOSL2, RTP3, and RASD1. The nomogram showed good performance in the prediction of MASH and it had clinical utility in distinguishing MASH from simple steatosis.

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 Comprehensive analyses of transcriptomes induced by Lyme spirochete infection to CNS model system

2021 ◽  
Author(s):  
Shiyuan Wen ◽  
Xin Xu ◽  
Jing Kong ◽  
Lisha Luo ◽  
Peng Yue ◽  
...  
Keyword(s):  
Lyme Disease ◽  
Ex Vivo ◽  
Clinical Manifestations ◽  
Interaction Network ◽  
Gene Expression Omnibus ◽  
Control Group ◽  
Mrna Levels ◽  
Hub Genes ◽  
Lyme Spirochete

Abstract Background Lyme disease is a zoonotic disease caused by infection with Borrelia burgdorferi (Bb), the involvement of the nervous system in Lyme disease is usually referred to as Lyme neuroborreliosis (LNB). LNB has diverse clinical manifestations, most commonly including meningitis, Bell’s palsy, and encephalitis. However, the molecular pathogenesis of neuroborreliosis is still poorly understood. Comprehensive transcriptomic analysis following Bb infection could provide new insights into the pathogenesis of LNB and may identify novel biomarkers or therapeutic targets for LNB diagnosis and treatment. Methods In the present study, we pooled transcriptomic datasets (transcriptomic rhesus data from our laboratory and the GSE85143 dataset from the Gene Expression Omnibus database) to screen common differentially expressed genes (DEGs) in the Bb infection group and the control group. Functional and enrichment analyses were conducted using the Database of Annotation Visualization and Integrated Discovery database, Protein-Protein Interaction network, and hub genes were identified using the Search Tool for the Retrieval of Interaction Genes database and the CytoHubba plugin. In addition, in vitro and ex vivo assays were performed to verify the above findings. The mRNA expression levels of these genes were verified by quantitative real-time PCR (qPCR). Results A total of 80 upregulated DEGs and 32 downregulated DEGs were identified. Among them, 11 hub genes were selected. Upregulated genes in the Gene Ontology analysis were significantly enriched in cell adhesion processes. The pathway enrichment analyses revealed that the PI3K-Akt signaling pathway was significantly enriched. The mRNA levels of ANGPT1, TLR6, SREBF1, LDLR, TNC, and ITGA2 in U251 cells and/or rhesus brain explants by exposure to Bb were validated by qPCR. Conclusion Our study suggested that TLR6, ANGPT1, LDLR, SREBF1, TNC, and ITGA were differentially highly expressed in Bb-infected astrocytes compared to normal controls, and overexpression of LDLR might be a favorable prognostic factor of LNB patients. Further study is needed to explore the value of TLR6, ANGPT1, LDLR, SREBF1, TNC, and ITGA in LNB pathogenesis.

scholarly journals Identification of the hub genes in gastric cancer through weighted gene co-expression network analysis

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e10682
Author(s):  
Chunyang Li ◽  
Haopeng Yu ◽  
Yajing Sun ◽  
Xiaoxi Zeng ◽  
Wei Zhang
Keyword(s):  
Neural Network ◽  
Gastric Cancer ◽  
Artificial Neural Network ◽  
Network Analysis ◽  
Candidate Genes ◽  
Characteristic Curve ◽  
Gene Expression Omnibus ◽  
Training Set ◽  
Hub Genes ◽  
Artificial Neural

Background Gastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy. Methods GSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the “least absolute shrinkage and selection operator” (LASSO) logistic regression. Finally, hub genes were validated in the GSE54129 dataset from GEO by supervised learning method artificial neural network (ANN) algorithm. Results Twelve modules with strong preservation were identified by using WGCNA methods in training set. Of which, five modules significantly related to gastric cancer were selected as clinically significant modules, and 713 candidate genes were identified from these five modules. Then, ADIPOQ, ARHGAP39, ATAD3A, C1orf95, CWH43, GRIK3, INHBA, RDH12, SCNN1G, SIGLEC11 and LYVE1 were screened as the hub genes. These hub genes successfully differentiated the tumor samples from the healthy tissues in an independent testing set through artificial neural network algorithm with the area under the receiver operating characteristic curve at 0.946. Conclusions These hub genes bearing diagnostic and therapeutic values, and our results may provide a novel prospect for the diagnosis and treatment of gastric cancer in the future.

scholarly journals BPI and KIR6.1 as significant hub genes for vein graft restenosis

2020 ◽  
Vol 48 (11) ◽  
pp. 030006052096933
Author(s):  
Yun-peng Bai ◽  
Bo-chen Yao ◽  
Mei Wang ◽  
Xian-kun Liu ◽  
Xiao-long Zhu ◽  
...  
Keyword(s):  
Vein Graft ◽  
Area Under The Curve ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Control Group ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Protein Protein Interaction

Background Vein graft restenosis (VGR), which appears to be caused by dyslipidemia following vascular transplantation, seriously affects the prognosis and long-term quality of life of patients. Methods This study analyzed the genetic data of restenosis (VGR group) and non-stenosis (control group) vessels from patients with coronary heart disease post-vascular transplantation and identified hub genes that might be responsible for its occurrence. GSE110398 was downloaded from the Gene Expression Omnibus database. A repeatability test for the GSE110398 dataset was performed using R language. This included the identification of differentially expressed genes (DEGs), enrichment analysis via Metascape software, pathway enrichment analysis, and construction of a protein–protein interaction network and a hub gene network. Results Twenty-four DEGs were identified between VGR and control groups. The four most important hub genes ( KIR6.1, PCLP1, EDNRB, and BPI) were identified, and Pearson’s correlation coefficient showed that KIR6.1 and BPI were significantly correlated with VGR. KIR6.1 could also sensitively predict VGR (0.9 < area under the curve ≤1). Conclusion BPI and KIR6.1 were differentially expressed in vessels with and without stenosis after vascular transplantation, suggesting that these genes or their encoded proteins may be involved in the occurrence of VGR.

scholarly journals Identification of 10 Hub genes related to the progression of colorectal cancer by co-expression analysis

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e9633
Author(s):  
Jie Meng ◽  
Rui Su ◽  
Yun Liao ◽  
Yanyan Li ◽  
Ling Li
Keyword(s):  
Colorectal Cancer ◽  
Network Analysis ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Data Sets ◽  
Ppi Network ◽  
Hub Genes ◽  
Normal Tissues ◽  
Go Enrichment ◽  
Go Enrichment Analysis

Background Colorectal cancer (CRC) is the third most common cancer in the world. The present study is aimed at identifying hub genes associated with the progression of CRC. Method The data of the patients with CRC were obtained from the Gene Expression Omnibus (GEO) database and assessed by weighted gene co-expression network analysis (WGCNA), Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses performed in R by WGCNA, several hub genes that regulate the mechanism of tumorigenesis in CRC were identified. Differentially expressed genes in the data sets GSE28000 and GSE42284 were used to construct a co-expression network for WGCNA. The yellow, black and blue modules associated with CRC level were filtered. Combining the co-expression network and the PPI network, 15 candidate hub genes were screened. Results After validation using the TCGA-COAD dataset, a total of 10 hub genes (MT1X, MT1G, MT2A, CXCL8, IL1B, CXCL5, CXCL11, IL10RA, GZMB, KIT) closely related to the progression of CRC were identified. The expressions of MT1G, CXCL8, IL1B, CXCL5, CXCL11 and GZMB in CRC tissues were higher than normal tissues (p-value < 0.05). The expressions of MT1X, MT2A, IL10RA and KIT in CRC tissues were lower than normal tissues (p-value < 0.05). Conclusions By combinating with a series of methods including GO enrichment analysis, KEGG pathway analysis, PPI network analysis and gene co-expression network analysis, we identified 10 hub genes that were associated with the progression of CRC.

scholarly journals Bioinformatics Analysis and Identification of Genes and Pathways in Ischemic Cardiomyopathy

2021 ◽  
Author(s):  
Jing Cao ◽  
Zhaoya Liu ◽  
Jie Liu ◽  
Chan Li ◽  
Guogang Zhang ◽  
...  
Keyword(s):  
Network Analysis ◽  
Ischemic Cardiomyopathy ◽  
Bioinformatics Analysis ◽  
Kegg Pathway ◽  
Gene Expression Omnibus ◽  
Hub Genes ◽  
Ppi Networks ◽  
Regulated Expression ◽  
Limma Package ◽  
Pathway Analyses

Abstract BackgroundIschemic cardiomyopathy (ICM) is considered to be the common cause of heart failure, which has high prevalence and mortality. This study aimed to investigate the different expressed genes (DEGs) and pathways in the pathogenesis of ICM using bioinformatics analysis.MethodsThe control and ICM datasets GSE116250，GSE46224 and GSE5406 were collected from the gene expression omnibus (GEO) database. DEGs were identified using limma package of R software and co-expressed genes were identified with Venn diagrams. Then, the gene otology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to explored the biological functions and signaling pathways. Protein-protein interaction (PPI) networks were assembled with Cytoscape software to identify hub genes related to the pathogenesis of ICM.ResultsA total of 844 DEGs were screened from GSE116250, of which 447 up-regulated and 397 down-regulated genes respectively. A total of 99 DEGs were singled out from GSE46224, of which 58 up-regulated and 41 down-regulated genes respectively. 30 DEGs were screened from GSE5406, including 10 genes with up-regulated expression and 20 genes with down-regulated expression. 5 up-regulated and 3 down-regulated co-expressed DEGs were intersected in three datasets. GO and KEGG pathway analyses revealed that DEGs mainly enriched in collagen fibril organization, protein digestion and absorption, AGE-RAGE signaling pathway and other related pathways. Collagen alpha-1(III) chain (COL3A1), collagen alpha-2(I) chain (COL1A2) and lumican (LUM) are the three hub genes in all three datasets through PPI network analysis. The expression of 5 DEGs (SERPINA3, FCN3, COL3A1, HBB, MXRA5) in heart tissues by qRT-PCR results were consistent with our GEO analysis, while expression of 3 DEGs (ASPN, LUM, COL1A2) were opposite with GEO analysis.ConclusionsThese findings from this bioinformatics network analysis investigated key hub genes, which contributed to better understand the mechanism and new therapeutic targets of ICM.

Identification and Validation of the Hub Genes in Gastric Cancer Through Weighted Gene Co-Expression Network Analysis

2020 ◽  
Author(s):  
Chunyang Li ◽  
Haopeng Yu ◽  
Yajing Sun ◽  
Xiaoxi Zeng ◽  
Wei Zhang
Keyword(s):  
Gastric Cancer ◽  
Logistic Regression ◽  
Network Analysis ◽  
Candidate Genes ◽  
Characteristic Curve ◽  
Gene Expression Omnibus ◽  
Training Set ◽  
Hub Genes ◽  
Clinically Significant ◽  
Logistic Regression Algorithm

Abstract Background: Gastric cancer is one of the most lethal tumors and is characterized by poor prognosis and lack of effective diagnostic or therapeutic biomarkers. The aim of this study was to find hub genes serving as biomarkers in gastric cancer diagnosis and therapy. Methods: GSE66229 from Gene Expression Omnibus (GEO) was used as training set. Genes bearing the top 25% standard deviations among all the samples in training set were performed to systematic weighted gene co-expression network analysis (WGCNA) to find candidate genes. Then, hub genes were further screened by using the “least absolute shrinkage and selection operator” (LASSO) logistic regression. Finally, hub genes were validated in GS54129 dataset from GEO by supervised learning methods logistic regression algorithms.Results: 12 modules with strong preservation were identified by using WGCNA methods in training set. Of which, two modules significantly related to gastric cancer were selected as clinically significant modules, and 43 candidate genes were identified from these two modules. Then, ACADL, ADIPOQ, ARHGAP39, ATAD3A, C1orf95, CCKBR, GRIK3, SCNN1G, SIGLEC11, and TXLNB were screened as the hub genes. These hub genes successfully differentiated the tumor samples from the healthy tissues in an independent testing set through the logistic regression algorithm, with the area under the receiver operating characteristic curve at 0.882. Conclusions: These hub genes bearing diagnostic and therapeutic values, and our results may provide a novel prospect for the diagnosis and treatment of gastric cancer in the future.

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