In Silico Analyses for Key Genes and Molecular Genetic Mechanism in Epilepsy and Alzheimer’s Disease

2018 ◽  
Vol 17 (8) ◽  
pp. 608-617 ◽  
Author(s):  
Xiao-wen Jiang ◽  
Hong-yuan Lu ◽  
Ziru Xu ◽  
Tian-yi Liu ◽  
Qiong Wu ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Circadian Rhythms ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Molecular Genetic ◽  
Differentially Expressed ◽  
Genetic Mechanism ◽  
Pathway Enrichment ◽  
Key Genes

Background: Epilepsy and Alzheimer's disease are common neuropathies with a complex pathogenesis. Both of them have some correlations in etiology, pathogenesis, pathological changes, clinical manifestations and treatment. Objective: This study investigated the key genes and molecular genetic mechanism in epilepsy and Alzheimer’s disease by bioinformatics analysis. Method: Two gene expression profiles were used to screen differentially expressed genes by GEO2R tool. The Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analysis was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Then the protein-protein interaction (PPI) network was constructed by Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software which can be used to analyze modules with MCODE. Results: A total of 199 differentially expressed genes (DEGs) in the two groups. According to GO_BP analysis and KEGG pathway enrichment by DAVID, we found DEGs referring to several pathways significantly down-regulated in endocytosis, such as endocytosis, synaptic vesicle cycle, lysosome, cAMP signaling pathway, circadian entrainment, LTP, glutamatergic synapse and GABAergic synapse pathway. The regulator genes of the upstream pathway of circadian rhythms were obviously downgraded. Conclusion: Our research demonstrated that the regulatory genes of the upstream pathway of circadian rhythms were obviously downgraded. These biological pathways and DEGs or hub genes may contribute to revealing the molecular relationship between Alzheimer's disease and epilepsy.

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.

scholarly journals Bioinformatics analysis for the identification of differentially expressed genes and related signaling pathways in H. pylori-CagA transfected gastric cancer cells

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e11203
Author(s):  
Dingyu Chen ◽  
Chao Li ◽  
Yan Zhao ◽  
Jianjiang Zhou ◽  
Qinrong Wang ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Helicobacter Pylori ◽  
Differentially Expressed Genes ◽  
Pathway Analysis ◽  
Kegg Pathway ◽  
Differentially Expressed ◽  
Ppi Network ◽  
Ags Cells ◽  
Kegg Pathway Analysis ◽  
Key Genes

Aim Helicobacter pylori cytotoxin-associated protein A (CagA) is an important virulence factor known to induce gastric cancer development. However, the cause and the underlying molecular events of CagA induction remain unclear. Here, we applied integrated bioinformatics to identify the key genes involved in the process of CagA-induced gastric epithelial cell inflammation and can ceration to comprehend the potential molecular mechanisms involved. Materials and Methods AGS cells were transected with pcDNA3.1 and pcDNA3.1::CagA for 24 h. The transfected cells were subjected to transcriptome sequencing to obtain the expressed genes. Differentially expressed genes (DEG) with adjusted P value < 0.05, — logFC —> 2 were screened, and the R package was applied for gene ontology (GO) enrichment and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. The differential gene protein–protein interaction (PPI) network was constructed using the STRING Cytoscape application, which conducted visual analysis to create the key function networks and identify the key genes. Next, the Kaplan–Meier plotter survival analysis tool was employed to analyze the survival of the key genes derived from the PPI network. Further analysis of the key gene expressions in gastric cancer and normal tissues were performed based on The Cancer Genome Atlas (TCGA) database and RT-qPCR verification. Results After transfection of AGS cells, the cell morphology changes in a hummingbird shape and causes the level of CagA phosphorylation to increase. Transcriptomics identified 6882 DEG, of which 4052 were upregulated and 2830 were downregulated, among which q-value < 0.05, FC > 2, and FC under the condition of ≤2. Accordingly, 1062 DEG were screened, of which 594 were upregulated and 468 were downregulated. The DEG participated in a total of 151 biological processes, 56 cell components, and 40 molecular functions. The KEGG pathway analysis revealed that the DEG were involved in 21 pathways. The PPI network analysis revealed three highly interconnected clusters. In addition, 30 DEG with the highest degree were analyzed in the TCGA database. As a result, 12 DEG were found to be highly expressed in gastric cancer, while seven DEG were related to the poor prognosis of gastric cancer. RT-qPCR verification results showed that Helicobacter pylori CagA caused up-regulation of BPTF, caspase3, CDH1, CTNNB1, and POLR2A expression. Conclusion The current comprehensive analysis provides new insights for exploring the effect of CagA in human gastric cancer, which could help us understand the molecular mechanism underlying the occurrence and development of gastric cancer caused by Helicobacter pylori.

P4-049: IDENTIFICATION OF CELL-SPECIFIC DIFFERENTIALLY EXPRESSED GENES IN ALZHEIMER'S DISEASE

2006 ◽  
Vol 14 (7S_Part_27) ◽  
pp. P1452-P1452
Author(s):  
Xue Wang ◽  
Mariet Allen ◽  
Minerva M. Carrasquillo ◽  
Jeremy D. Burgess ◽  
Shaoyu Li ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Differentially Expressed Genes ◽  
Differentially Expressed

Identification of Differentially Expressed Genes and Signaling Pathways Related to Ovarian Endometriosis by Integrated Bioinformatics Analysis

2020 ◽  
Author(s):  
Kainan Lin ◽  
Zhenyan Pan ◽  
Renke He ◽  
Hanchu Wang ◽  
Kai Zhou ◽  
...  
Keyword(s):  
Mrna Expression ◽  
Signaling Pathway ◽  
Differentially Expressed Genes ◽  
Kegg Pathway ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Differentially Expressed ◽  
Pathway Enrichment Analysis ◽  
Go Analysis ◽  
Pathway Enrichment

Abstract Purpose: Endometriosis was a common gynecological disease, however, the specific mechanism and the key molecules of endometriosis remained uncertain. This study aimed to single out key genes associated with poor prognosis, and further uncover underlying mechanisms.Methods: Data regarding mRNA expression profiles used in this study were retrieved from the Gene Expression Omnibus (GEO) database, a total of three mRNA expression profiles were included for subsequent analysis (GSE31515, GSE58178 and GSE120103). Then, we conducted Gene Ontology analysis (GO analysis), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) analysis by the software R.Results: A total of 304 differentially expressed genes (DEGs) between endometriosis tissues and normal endometrium tissues were identified in integrated analysis, including 185 up-regulated genes and 119 down-regulated genes. GO analysis reveals that the DEGs of endometriosis were closely associated with molecular origin of bacteria. KEGG pathway enrichment analysis indicates that the DEGs were mainly involved in AGE-RAGE signaling pathway in diabetic complications. In addition, PPI of these DEGs was visualized by Cytoscape platform with utilization of Search Tool for the Retrieval of Interacting Genes (STRING). PPI analysis identifies 10 potential DEGs-related protein targets, including CCND1, IL6, CCL2, COL1A2, PTGS2, VCAM1, COL3A1, ELN, SERPINE1, HSP90B1. Conclusion: In conclusion, the present study reveals that bacterial contamination, defect of female reproductive system development, retrograde menstruation and the AGE-RAGE signaling pathway may be involved in the development of endometriosis In addition, these identified DEGs may be of clinical significance for the diagnosis and treatment of the endometriosis.

scholarly journals Identification of potential biomarkers for pathogenesis of Alzheimer’s disease

Hereditas ◽  
2021 ◽  
Vol 158 (1) ◽  
Author(s):  
Huimin Wang ◽  
Xiujiang Han ◽  
Sheng Gao
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Neurodegenerative Disorder ◽  
Maximal Clique ◽  
Gene Expression Omnibus ◽  
Differentially Expressed ◽  
Kegg Pathways ◽  
Functional Connections ◽  
Key Genes ◽  
Potential Biomarkers

Abstract Background Alzheimer’s disease (AD) is an extremely complicated neurodegenerative disorder, which accounts for almost 80 % of all dementia diagnoses. Due to the limited treatment efficacy, it is imperative for AD patients to take reliable prevention and diagnosis measures. This study aimed to explore potential biomarkers for AD. Methods GSE63060 and GSE140829 datasets were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEG) between AD and control groups in GSE63060 were analyzed using the limma software package. The mRNA expression data in GSE140829 was analyzed using weighted gene co-expression network analysis (WGCNA) function package. Protein functional connections and interactions were analyzed using STRING and key genes were screened based on the degree and Maximal Clique Centrality (MCC) algorithm. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed on the key genes. Results There were 65 DEGs in GSE63060 dataset between AD patients and healthy controls. In GSE140829 dataset, the turquoise module was related to the pathogenesis of AD, among which, 42 genes were also differentially expressed in GSE63060 dataset. Then 8 genes, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were finally screened. Additionally, these 42 genes were significantly enriched in 12 KEGG pathways and 119 GO terms. Conclusions In conclusion, RPS17, RPL26, RPS3A, RPS25, EEF1B2, COX7C, HINT1 and SNRPG, were potential biomarkers for pathogenesis of AD, which should be further explored in AD in the future.

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