scholarly journals Functional Modules Analysis and Hub Gene Prognostic Values Evaluation Based on Co-Expression Network in Gastric Cancer

2021 ◽  
Author(s):  
Yujia Liu ◽  
Xiaoping Hu ◽  
Zongfu Pan ◽  
Yuchen Jiang ◽  
Dandan Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Transcriptional Regulatory Network ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Gene ◽  
Transcriptional Regulatory ◽  
Key Nodes

Abstract Background: Gastric cancer is one of the most common fatal disease worldwide, but its mechanism and therapeutic targets are still unclear. In this study, we have analyzed the differences in gene modules and key pathways in gastric cancer patients, then elaborated the mechanism and effective treatment of gastric cancer with microarray data from the gene expression omnibus(GEO) database. Methods: GEO2R tools were used to identify differential expression genes (DEGs), String database was employed to construct a protein-protein interaction (PPI) network. We imported the PPI network into the Cytoscape software to find key nodes, and employed statistical approach of MCODE to cluster genes. After that the ClueGO was used to enrich and annotate the pathways of key modules. To investigate the relationship between the upstream regulator and hub genes, the transcriptional regulatory network was built based on TFCAT database. Results: 63 characteristic genes of gastric cancer are involved in regulation of ECM-receptor interaction, focal adhesion and protein digestion and absorption. SPARC, FN1, BGN and COL1A2 are four key nodes relating to tumor proliferation and metastasis, and their expression were strongly associated with poor survival (p<0.05). 13 transcription factors including PRRX1 have remarkable changes in gastric cancer, which may play a key role in hub gene regulation. Conclusions: The present study defined the gene expression characteristics and transcriptional regulatory network that promote our understanding of the molecular mechanisms underlying the development of gastric cancer, and might provide new insights into targeted therapy and prognostic markers for the personalized treatment of gastric cancer.

scholarly journals Functional Modules Analysis and Hub Gene Prognostic Values Evaluation Based on Co-Expression Network in Gastric Cancer

2021 ◽  
Author(s):  
Yujia Liu ◽  
Xiaoping Hu ◽  
Zongfu Pan ◽  
Yuchen Jiang ◽  
Dandan Guo ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Transcriptional Regulatory Network ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Gene ◽  
Transcriptional Regulatory ◽  
Key Nodes

Abstract Background: Gastric cancer is one of the most common fatal disease worldwide, but its mechanism and therapeutic targets are still unclear. In this study, we have analyzed the differences in gene modules and key pathways in gastric cancer patients, then elaborated the mechanism and effective treatment of gastric cancer with microarray data from the gene expression omnibus(GEO) database.Methods: GEO2R tools were used to identify differential expression genes (DEGs), String database was employed to construct a protein-protein interaction (PPI) network. We imported the PPI network into the Cytoscape software to find key nodes, and employed statistical approach of MCODE to cluster genes. After that the ClueGO was used to enrich and annotate the pathways of key modules. To investigate the relationship between the upstream regulator and hub genes, the transcriptional regulatory network was built based on TFCAT database.Results: 63 characteristic genes of gastric cancer are involved in regulation of ECM-receptor interaction, focal adhesion and protein digestion and absorption. SPARC, FN1, BGN and COL1A2 are four key nodes relating to tumor proliferation and metastasis, and their expression were strongly associated with poor survival (p<0.05). 13 transcription factors including PRRX1 have remarkable changes in gastric cancer, which may play a key role in hub gene regulation.Conclusions: The present study defined the gene expression characteristics and transcriptional regulatory network that promote our understanding of the molecular mechanisms underlying the development of gastric cancer, and might provide new insights into targeted therapy and prognostic markers for the personalized treatment of gastric cancer.

scholarly journals Genetic and pharmacological inactivation of adenosine A2A receptor reveals an Egr-2-mediated transcriptional regulatory network in the mouse striatum

2005 ◽  
Vol 23 (1) ◽  
pp. 89-102 ◽  
Author(s):  
Liqun Yu ◽  
Peter M. Haverty ◽  
Juliana Mariani ◽  
Yumei Wang ◽  
Hai-Ying Shen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Mrna Level ◽  
Gene Expression Profiles ◽  
Bioinformatics Analyses ◽  
Mouse Striatum ◽  
Transcriptional Regulatory

The adenosine A2A receptor (A2AR) is highly expressed in the striatum, where it modulates motor and emotional behaviors. We used both microarray and bioinformatics analyses to compare gene expression profiles by genetic and pharmacological inactivation of A2AR and inferred an A2AR-controlled transcription network in the mouse striatum. A comparison between vehicle (VEH)-treated A2AR knockout (KO) mice (A2AR KO-VEH) and wild-type (WT) mice (WT-VEH) revealed 36 upregulated genes that were partially mimicked by treatment with SCH-58261 (SCH; an A2AR antagonist) and 54 downregulated genes that were not mimicked by SCH treatment. We validated the A2AR as a specific drug target for SCH by comparing A2AR KO-SCH and A2AR KO-VEH groups. The unique downregulation effect of A2AR KO was confirmed by comparing A2AR KO-SCH with WT-SCH gene groups. The distinct striatal gene expression profiles induced by A2AR KO and SCH should provide clues to the molecular mechanisms underlying the different phenotypes observed after genetic and pharmacological inactivation of A2AR. Furthermore, bioinformatics analysis discovered that Egr-2 binding sites were statistically overrepresented in the proximal promoters of A2AR KO-affected genes relative to the unaffected genes. This finding was further substantiated by the demonstration that the Egr-2 mRNA level increased in the striatum of both A2AR KO and SCH-treated mice and that striatal Egr-2 binding activity in the promoters of two A2AR KO-affected genes was enhanced in A2AR KO mice as assayed by chromatin immunoprecipitation. Taken together, these results strongly support the existence of an Egr-2-directed transcriptional regulatory network controlled by striatal A2ARs.

scholarly journals Screening Driving Transcription Factors in the Processing of Gastric Cancer

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Guangzhong Xu ◽  
Kai Li ◽  
Nengwei Zhang ◽  
Bin Zhu ◽  
Guosheng Feng
Keyword(s):  
Gastric Cancer ◽  
Transcription Factors ◽  
Regulatory Network ◽  
Target Genes ◽  
Transcriptional Regulatory Network ◽  
Expression Profiles ◽  
Functional Enrichment ◽  
Regulatory Mechanisms ◽  
Integrated Analysis ◽  
Transcriptional Regulatory

Background. Construction of the transcriptional regulatory network can provide additional clues on the regulatory mechanisms and therapeutic applications in gastric cancer.Methods. Gene expression profiles of gastric cancer were downloaded from GEO database for integrated analysis. All of DEGs were analyzed by GO enrichment and KEGG pathway enrichment. Transcription factors were further identified and then a global transcriptional regulatory network was constructed.Results. By integrated analysis of the six eligible datasets (340 cases and 43 controls), a bunch of 2327 DEGs were identified, including 2100 upregulated and 227 downregulated DEGs. Functional enrichment analysis of DEGs showed that digestion was a significantly enriched GO term for biological process. Moreover, there were two important enriched KEGG pathways: cell cycle and homologous recombination. Furthermore, a total of 70 differentially expressed TFs were identified and the transcriptional regulatory network was constructed, which consisted of 566 TF-target interactions. The top ten TFs regulating most downstream target genes were BRCA1, ARID3A, EHF, SOX10, ZNF263, FOXL1, FEV, GATA3, FOXC1, and FOXD1. Most of them were involved in the carcinogenesis of gastric cancer.Conclusion. The transcriptional regulatory network can help researchers to further clarify the underlying regulatory mechanisms of gastric cancer tumorigenesis.

scholarly journals Identification of Key Genes and MicroRNAs in Gastric Cancer via miRNA-mRNA Regulatory Network

2020 ◽  
Author(s):  
Chao Huang ◽  
Xiaojian Zhu ◽  
Jiefeng Zhao ◽  
Fanqin Bu ◽  
Jun Huang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Molecular Markers ◽  
Expression Profiling ◽  
Regulatory Network ◽  
Molecular Mechanisms ◽  
Critical Role ◽  
Differentially Expressed ◽  
Expression Levels ◽  
Receptor Interactions

Abstract Background Gastric cancer (GC) is a malignant tumor with high mortality. MicroRNAs (miRNAs) participate in various biological processes and disease pathogenesis by targeting messenger RNA (mRNA). The purpose of this study was to identify potential prognostic molecular markers of GC and to characterize the molecular mechanisms of GC. Methods A gene expression profiling dataset (GSE54129) and miRNA expression profiling dataset (GSE113486) were downloaded from the Gene Expression Omnibus (GEO) database. A miRNA-mRNA interaction network was established. Functional and pathway enrichment analyses were performed for differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) using FunRich, the clusterProfiler package, and DIANA-mirPath. Survival analysis of key molecular markers was performed using the online tool Kaplan-Meier Plotter and the database OncomiR. Finally, experiments were carried out to verify the expression levels and biological functions of a key gene. Results A total of 390 DEMs and 341 DEGs were identified. Ultimately, 45 genes and 31 miRNAs were selected to establish a miRNA-mRNA regulatory network. Four hub genes (ZFPM2, FUT9, NEUROD1 and LIPH) and six miRNAs (hsa-let-7d-5p, hsa-miR-23b-3p, hsa-miR-23a-3p, hsa-miR-133b, hsa-miR-130a-3p and hsa-miR-124-3p) were identified in the network. DEGs and DEMs were associated with ECM-receptor interactions and metabolic pathways. Two genes (ZFPM2 and LIPH) and two miRNAs (hsa-miR-23a-3p and hsa-miR-130a-3p) were observed to be related to the prognosis of GC. ZFPM2 was highly expressed in GC tissues and various GC cell lines and could promote the proliferation, invasion and migration of GC cells. Conclusion The expression levels of ZFPM2, LIPH, hsa-miR-23a-3p and hsa-miR-130a-3p were closely related to the prognosis of GC. ZFPM2 may serve as a potential molecular marker and therapeutic target for GC. ECM receptor interactions and metabolic abnormalities play a critical role in the GC progression.

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 Identification of transcriptional regulatory network associated with response of host epithelial cells to SARS-CoV-2

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chen Su ◽  
Simon Rousseau ◽  
Amin Emad
Keyword(s):  
Gene Expression ◽  
Epithelial Cells ◽  
Regulatory Network ◽  
Transcriptional Regulatory Network ◽  
Organ Damage ◽  
Host Cells ◽  
Regulatory Mechanisms ◽  
Transcriptional Regulatory ◽  
Transcriptional Regulatory Mechanisms ◽  
The Impact

AbstractIdentification of transcriptional regulatory mechanisms and signaling networks involved in the response of host cells to infection by SARS-CoV-2 is a powerful approach that provides a systems biology view of gene expression programs involved in COVID-19 and may enable the identification of novel therapeutic targets and strategies to mitigate the impact of this disease. In this study, our goal was to identify a transcriptional regulatory network that is associated with gene expression changes between samples infected by SARS-CoV-2 and those that are infected by other respiratory viruses to narrow the results on those enriched or specific to SARS-CoV-2. We combined a series of recently developed computational tools to identify transcriptional regulatory mechanisms involved in the response of epithelial cells to infection by SARS-CoV-2, and particularly regulatory mechanisms that are specific to this virus when compared to other viruses. In addition, using network-guided analyses, we identified kinases associated with this network. The results identified pathways associated with regulation of inflammation (MAPK14) and immunity (BTK, MBX) that may contribute to exacerbate organ damage linked with complications of COVID-19. The regulatory network identified herein reflects a combination of known hits and novel candidate pathways supporting the novel computational pipeline presented herein to quickly narrow down promising avenues of investigation when facing an emerging and novel disease such as COVID-19.

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