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 Meta-Analyses of Multiple Gene Expression Profiles to Screen Hub Genes Related to Osteoarthritis

2021 ◽  
Vol 24 (5-6) ◽  
pp. 267-279
Author(s):  
Xianyang Zhu ◽  
Wen Guo
Keyword(s):  
Gene Expression ◽  
Molecular Mechanisms ◽  
Expression Profiles ◽  
Meta Analysis ◽  
Wnt Signaling Pathway ◽  
Gene Expression Profiles ◽  
Enrichment Analysis ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes

<b><i>Background:</i></b> This study aimed to screen and validate the crucial genes involved in osteoarthritis (OA) and explore its potential molecular mechanisms. <b><i>Methods:</i></b> Four expression profile datasets related to OA were downloaded from the Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) from 4 microarray patterns were identified by the meta-analysis method. The weighted gene co-expression network analysis (WGCNA) method was used to investigate stable modules most related to OA. In addition, a protein-protein interaction (PPI) network was built to explore hub genes in OA. Moreover, OA-related genes and pathways were retrieved from Comparative Toxicogenomics Database (CTD). <b><i>Results:</i></b> A total of 1,136 DEGs were identified from 4 datasets. Based on these DEGs, WGCNA further explored 370 genes included in the 3 OA-related stable modules. A total of 10 hub genes were identified in the PPI network, including <i>AKT1</i>, <i>CDC42</i>, <i>HLA-DQA2</i>, <i>TUBB</i>, <i>TWISTNB</i>, <i>GSK3B</i>, <i>FZD2</i>, <i>KLC1</i>, <i>GUSB</i>, and <i>RHOG</i>. Besides, 5 pathways including “Lysosome,” “Pathways in cancer,” “Wnt signaling pathway,” “ECM-receptor interaction” and “Focal adhesion” in CTD and enrichment analysis and 5 OA-related hub genes (including <i>GSK3B, CDC42, AKT1, FZD2</i>, and <i>GUSB</i>) were identified. <b><i>Conclusion:</i></b> In this study, the meta-analysis was used to screen the central genes associated with OA in a variety of gene expression profiles. Three OA-related modules (green, turquoise, and yellow) containing 370 genes were identified through WGCNA. It was discovered through the gene-pathway network that <i>GSK3B, CDC42, AKT1, FZD2</i>, <i>and GUSB</i> may be key genes related to the progress of OA and may become promising therapeutic targets.

scholarly journals IGFBP-2 as a biomarker in NAFLD improves hepatic steatosis:an integrated bioinformatics and experimental study

2021 ◽  
Author(s):  
Xu Chen ◽  
Yi Tang ◽  
Shen Chen ◽  
Wen-Hua Ling ◽  
Qing Wang
Keyword(s):  
Liver Disease ◽  
Hepatic Steatosis ◽  
Molecular Mechanisms ◽  
Gene Expression Omnibus ◽  
Ppi Network ◽  
Hub Genes ◽  
Alcoholic Fatty Liver ◽  
Simple Steatosis ◽  
Public Datasets ◽  
Geo Database

Background and aims: Non-alcoholic fatty liver disease (NAFLD) has become a common chronic liver disease in the world. Simple steatosis is the early phase of NAFLD. However, the molecular mechanisms underlying the development of steatosis have not yet been fully elucidated. Methods: Two public datasets (GSE48452 and GSE89632) through the Gene Expression Omnibus (GEO) database were used to identify differentially expressed genes (DEGs) in the development of steatosis. A total of 72 participants including 38 normal histological controls and 34 simple steatosis patients were included in this study. GO, KEGG and PPI network analysis were performed to explore the function of DEGs. The results were further confirmed in high-fat diet (HFD)-fed mice and oleate-treated HepG2 cells. Results: Total 57 DEGs including 31 up- and 26 down-regulated genes between simple steatosis patients and healthy controls were determined. GO and KEGG analysis showed that most of DEGs were enriched in the ligand-receptor signaling pathways. PPI network construction was used to identify the hub genes of the DEGs. MYC, ANXA2, GDF15, AGTR1, NAMPT, LEPR, IGFBP-2, IL1RN, MMP7 and APLNR were identified as hub genes. And IGFBP-2 expression was found to be reversely associated with hepatic steatosis, fasting insulin, HOMA-IR index and ALT levels. In HFD-fed mice, hepatic IGFBP-2 was also downregulated and negatively associated with hepatic triglyceride levels. Moreover, overexpression IGFBP-2 ameliorated the oleate induced accumulation of triglycerides in hepatocytes. Conclusions: This study identified novel gene signatures in the hepatic steatosis and will provide new understanding and molecular clues of hepatic steatosis.

Identification of Genes and Pathways Associated with Acne Using Integrated Bioinformatics Methods

Dermatology ◽  
2019 ◽  
Vol 235 (6) ◽  
pp. 445-455 ◽  
Author(s):  
Xianglan Li ◽  
Yuxi Jia ◽  
Shiyi Wang ◽  
Tianqi Meng ◽  
Mingji Zhu
Keyword(s):  
Expression Profiles ◽  
Killer Cell ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Cytokine Receptor ◽  
Gene Set Enrichment Analysis ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Ncbi Gene Expression Omnibus ◽  
Chemokine Signaling

Background: Acne is the most common skin inflammatory condition. The pathogenesis of acne is not fully understood. Aims: We performed weighted gene co-expression network analysis (WGCNA) to select acne-associated genes and pathways. Methods: GSE53795 and GSE6475 datasets including data from lesional and nonlesional skin of acne patients were downloaded from the NCBI Gene Expression Omnibus. Differentially expressed genes (DEGs) in lesions were identified following a false discovery rate <0.05 and | log2 fold change | ≥0.5. DEG-associated biological processes and pathways were identified. WGCNA analysis was performed to identify acne-associated modules. DEGs in the acne-associated modules were used for protein-protein interaction (PPI) network construction and Gene Set Enrichment Analysis (GSEA). Acne-associated candidate DEGs and pathways were identified together with items in the Comparative Toxicogenomics Database (CTD). Results: A total of 2,140 and 1,190 DEGs were identified in GSE53795 and GSE6475 datasets, respectively, including 716 overlapping DEGs with similar expression profiles in the two datasets, which were clustered into 10 consensus modules. Two modules (brown and turquoise, 359 genes) were associated with acne phenotype. Of these 359 DEGs, 254 were enrolled in the PPI network. GSEA showed that these DEGs were associated with chemokine signaling pathway, cytokine-cytokine receptor interaction, and natural killer cell-mediated cytotoxicity. After identification in CTD, one pathway Cytokine-cytokine receptor interaction and 24 acne-associated DEGs, including IL1R1, CXCL1, CXCR4, CCR1, CXCL2 and IL1β, were identified as candidates associated with acne. Conclusion: Our results highlight the important roles of the proinflammatory cytokines including IL1β, CXCL1, CXCL2, CXCR4, and CCR1 in acne pathogenesis or therapeutic management.

scholarly journals Identification of Potential Key Genes and Pathways in Enzalutamide-Resistant Prostate Cancer Cell Lines: A Bioinformatics Analysis with Data from the Gene Expression Omnibus (GEO) Database

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Long Zheng ◽  
Xiaojie Dou ◽  
Xiaodong Ma ◽  
Wei Qu ◽  
Xiaoshuang Tang
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cell Lines ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Gene Set Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Key Genes ◽  
Geo Database

Enzalutamide (ENZ) has been approved for the treatment of advanced prostate cancer (PCa), but some patients develop ENZ resistance initially or after long-term administration. Although a few key genes have been discovered by previous efforts, the complete mechanisms of ENZ resistance remain unsolved. To further identify more potential key genes and pathways in the development of ENZ resistance, we employed the GSE104935 dataset, including 5 ENZ-resistant (ENZ-R) and 5 ENZ-sensitive (ENZ-S) PCa cell lines, from the Gene Expression Omnibus (GEO) database. Integrated bioinformatics analyses were conducted, such as analysis of differentially expressed genes (DEGs), Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, protein-protein interaction (PPI) analysis, gene set enrichment analysis (GSEA), and survival analysis. From these, we identified 201 DEGs (93 upregulated and 108 downregulated) and 12 hub genes (AR, ACKR3, GPER1, CCR7, NMU, NDRG1, FKBP5, NKX3-1, GAL, LPAR3, F2RL1, and PTGFR) that are potentially associated with ENZ resistance. One upregulated pathway (hedgehog pathway) and seven downregulated pathways (pathways related to androgen response, p53, estrogen response, TNF-α, TGF-β, complement, and pancreas β cells) were identified as potential key pathways involved in the occurrence of ENZ resistance. Our findings may contribute to further understanding the molecular mechanisms of ENZ resistance and provide some clues for the prevention and treatment of ENZ resistance.

scholarly journals Bioinformatics analysis of microarray data to identify the candidate biomarkers of lung adenocarcinoma

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7313 ◽  
Author(s):  
Tingting Guo ◽  
Hongtao Ma ◽  
Yubai Zhou
Keyword(s):  
Survival Analysis ◽  
Lung Adenocarcinoma ◽  
Molecular Mechanisms ◽  
Prion Diseases ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes

Background Lung adenocarcinoma (LUAD) is the major subtype of lung cancer and the most lethal malignant disease worldwide. However, the molecular mechanisms underlying LUAD are not fully understood. Methods Four datasets (GSE118370, GSE85841, GSE43458 and GSE32863) were obtained from the gene expression omnibus (GEO). Identification of differentially expressed genes (DEGs) and functional enrichment analysis were performed using the limma and clusterProfiler packages, respectively. A protein–protein interaction (PPI) network was constructed via Search Tool for the Retrieval of Interacting Genes (STRING) database, and the module analysis was performed by Cytoscape. Then, overall survival analysis was performed using the Kaplan–Meier curve, and prognostic candidate biomarkers were further analyzed using the Oncomine database. Results Totally, 349 DEGs were identified, including 275 downregulated and 74 upregulated genes which were significantly enriched in the biological process of extracellular structure organization, leukocyte migration and response to peptide. The mainly enriched pathways were complement and coagulation cascades, malaria and prion diseases. By extracting key modules from the PPI network, 11 hub genes were screened out. Survival analysis showed that except VSIG4, other hub genes may be involved in the development of LUAD, in which MYH10, METTL7A, FCER1G and TMOD1 have not been reported previously to correlated with LUAD. Briefly, novel hub genes identified in this study will help to deepen our understanding of the molecular mechanisms of LUAD carcinogenesis and progression, and to discover candidate targets for early detection and treatment of LUAD.

scholarly journals Prognostic values and prospective pathway signaling of MicroRNA-182 in ovarian cancer: a study based on gene expression omnibus (GEO) and bioinformatics analysis

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Yaowei Li ◽  
Li Li
Keyword(s):  
Gene Expression ◽  
Target Genes ◽  
Gynecological Cancer ◽  
Enrichment Analysis ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
Pathway Enrichment Analysis ◽  
Ppi Network ◽  
Hub Genes ◽  
Protein Protein Interaction

Abstract Background Ovarian carcinoma (OC) is a common cause of death among women with gynecological cancer. MicroRNAs (miRNAs) are believed to have vital roles in tumorigenesis of OC. Although miRNAs are broadly recognized in OC, the role of has-miR-182-5p (miR-182) in OC is still not fully elucidated. Methods We evaluated the significance of miR-182 expression in OC by using analysis of a public dataset from the Gene Expression Omnibus (GEO) database and a literature review. Furthermore, we downloaded three mRNA datasets of OC and normal ovarian tissues (NOTs), GSE14407, GSE18520 and GSE36668, from GEO to identify differentially expressed genes (DEGs). Then the targeted genes of hsa-miR-182-5p (TG_miRNA-182-5p) were predicted using miRWALK3.0. Subsequently, we analyzed the gene overlaps integrated between DEGs in OC and predicted target genes of miR-182 by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and the prognostic effects of the hub genes were analyzed. Results A common pattern of up-regulation for miR-182 in OC was found in our review of the literature. A total of 268 DEGs, both OC-related and miR-182-related, were identified, of which 133 genes were discovered from the PPI network. A number of DEGs were enriched in extracellular matrix organization, pathways in cancer, focal adhesion, and ECM-receptor interaction. Two hub genes, MCM3 and GINS2, were significantly associated with worse overall survival of patients with OC. Furthermore, we identified covert miR-182-related genes that might participate in OC by network analysis, such as DCN, AKT3, and TIMP2. The expressions of these genes were all down-regulated and negatively correlated with miR-182 in OC. Conclusions Our study suggests that miR-182 is essential for the biological progression of OC.

scholarly journals Identification of Key Modules and Hub Genes in Hypertrophic Cardiomyopathy Based on Integrative Weighted Gene Co-expression Network Analysis

2021 ◽  
Author(s):  
Jun Jiang ◽  
Delong Chen ◽  
Siyuan Xie ◽  
Qichao Dong ◽  
Yi Yu ◽  
...  
Keyword(s):  
Network Analysis ◽  
Hedgehog Signaling ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Hub Genes ◽  
Remodeling Of Extracellular Matrix

Abstract BackgroundHypertrophic cardiomyopathy (HCM) is a heterogeneously inherited cardiac disorder with unclear biological pathogenesis. This study aims to identify the key modules and genes involved in the development of HCM.MethodsUsing weighted gene co-expression network analysis (WGCNA) algorithm, we constructed integrative co-expression networks for the two large sample HCM datasets separately. After selecting clinically significant modules with the same clinical trait, functional enrichment analysis was performed to detect their common pathways. Based on the intramodular connectivity (IC), the shared hub genes were generated, validated, and further explored in gene set enrichment analysis (GSEA).ResultsThe orange and pink modules in GSE141910, the green and brown modules in GSE36961 were mostly related to HCM. Functional enrichment analysis suggested that HCM might exhibit enhanced processes including remodeling of extracellular matrix, activation of abnormal protein signaling, aggregation of calcium ion, and organization of cytoskeleton. SMOC2, COL16A1, RASL11B, TUBA3D, IL18R1 were defined as real hub genes due to their top IC values, significantly different expression levels, and excellent diagnostic performance in both datasets. Moreover, GSEA analysis demonstrated that pathways of the five hub genes were mainly involved in neuroactive ligand-receptor interaction, ECM-receptor interaction, Hedgehog signaling pathway.ConclusionOur study provides more comprehensive insights into the molecular mechanisms of HCM, identifies five hub genes as candidate biomarkers for HCM, which might be theoretically feasible for targeted therapy against HCM.

scholarly journals Bioinformatic analysis identifies potential key genes of epilepsy

PLoS ONE ◽  
2021 ◽  
Vol 16 (9) ◽  
pp. e0254326
Author(s):  
Yike Zhu ◽  
Dan Huang ◽  
Zhongyan Zhao ◽  
Chuansen Lu
Keyword(s):  
Network Analysis ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Bioinformatic Analysis ◽  
Gene Expression Omnibus ◽  
Functional Enrichment ◽  
P Value ◽  
Ppi Network ◽  
Functional Roles ◽  
Protein Protein Interaction

Background Epilepsy is one of the most common brain disorders worldwide. It is usually hard to be identified properly, and a third of patients are drug-resistant. Genes related to the progression and prognosis of epilepsy are particularly needed to be identified. Methods In our study, we downloaded the Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE143272. Differentially expressed genes (DEGs) with a fold change (FC) >1.2 and a P-value <0.05 were identified by GEO2R and grouped in male, female and overlapping DEGs. Functional enrichment analysis and Protein-Protein Interaction (PPI) network analysis were performed. Results In total, 183 DEGs overlapped (77 ups and 106 downs), 302 DEGs (185 ups and 117 downs) in the male dataset, and 750 DEGs (464 ups and 286 downs) in the female dataset were obtained from the GSE143272 dataset. These DEGs were markedly enriched under various Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms. 16 following hub genes were identified based on PPI network analysis: ADCY7, C3AR1, DEGS1, CXCL1 in male-specific DEGs, TOLLIP, ORM1, ELANE, QPCT in female-specific DEGs and FCAR, CD3G, CLEC12A, MOSPD2, CD3D, ALDH3B1, GPR97, PLAUR in overlapping DEGs. Conclusion This discovery-driven study may be useful to provide a novel insight into the diagnosis and treatment of epilepsy. However, more experiments are needed in the future to study the functional roles of these genes in epilepsy.

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 Identification of Biomarkers and Study of Mechanisms Related to Metastatic of Osteosarcoma Based on Integrated Bioinformatics Analyses

2020 ◽  
Author(s):  
Guangzhi Wu ◽  
Minglei Zhang
Keyword(s):  
Clinical Characteristics ◽  
Molecular Mechanisms ◽  
Bioinformatics Analysis ◽  
Gene Expression Omnibus ◽  
Receptor Interaction ◽  
High Morbidity ◽  
Comparative Toxicogenomics Database ◽  
Bioinformatics Analyses ◽  
Kegg Pathways ◽  
Geo Database

Abstract BackgroundOsteosarcoma (OS) is a serious threat to public health. Because of high morbidity and fairly complicated pathogenesis. The study aim to identify candidate biomarkers and research the molecular mechanisms correlated of patients with metastatic OS. MethodsThe GSE21257 was downloaded from Gene Expression Omnibus(GEO) database, and the differentially expressed RNAs (DERs) were identified and functional enriched analysis by statistical soft-ware in R. Subsequently, the co-expression modules and its clinical characteristics of OS were identified by weighted gene co-expression network analysis (WGCNA) Following, the KEGG pathways directly related to metastatic OS was to researched by the Comparative Toxicogenomics Database 2019 update (CTD). Finally, the “survival” package in R was used to survival analysis and the DERs were verified using another independent profiling GSE14827. ResultsA total of 1,464 DERs were classified including 702 up-regulated and 762 down-regulated. In addition, a total of 1248 DERs were obtained by WGCNA analysis, the blue modules is the highest negative correlation (P=0) and the turquoise modules is highest positive correlation (P=3E-196) among all correlations with OS metastatic. The lncRNA-mRNA co-expression network including 4 lncRNAs and 507 mRNAs, and the cytokine-cytokine receptor interaction and JAK-STAT signaling pathway were found significantly correlation with metastatic. Finally, the increased expression levels of IFNGR1, lower DLEU1 and DLEU2 related to better prognosis. Which were significantly consistent in the another independent profiling GSE14827. ConclusionsA bioinformatics analysis related to the IFNGR1, DLEU1 and DLEU2 may as candidate biomarkers for metastatic OS.

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