scholarly journals Identification of Parkinson’s disease-related pathways and potential risk factors

2020 ◽  
Vol 48 (10) ◽  
pp. 030006052095719
Author(s):  
Jun Shen ◽  
Xiao-Chang Chen ◽  
Wang-Jun Li ◽  
Qiu Han ◽  
Chun Chen ◽  
...  
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Actin Cytoskeleton ◽  
Cytokine Receptor ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Svm Model

Objective To identify Parkinson’s disease (PD)-associated deregulated pathways and genes, to further elucidate the pathogenesis of PD. Methods Dataset GSE100054 was downloaded from the Gene Expression Omnibus, and differentially expressed genes (DEGs) in PD samples were identified. Functional enrichment analyses were conducted for the DEGs. The top 10 hub genes in the protein–protein interaction (PPI) network were screened out and used to construct a support vector machine (SVM) model. The expression of the top 10 genes was then validated in another dataset, GSE46129, and a clinical patient cohort. Results A total of 333 DEGs were identified. The DEGs were clustered into two gene sets that were significantly enriched in 12 pathways, of which 8 were significantly deregulated in PD, including cytokine–cytokine receptor interaction, gap junction, and actin cytoskeleton regulation. The signature of the top 10 hub genes in the PPI network was used to construct the SVM model, which had high performance for predicting PD. Of the 10 genes, GP1BA, GP6, ITGB5, and P2RY12 were independent risk factors of PD. Conclusion Genes such as GP1BA, GP6, P2RY12, and ITGB5 play critical roles in PD pathology through pathways including cytokine−cytokine receptor interaction, gap junctions, and actin cytoskeleton regulation.

scholarly journals Identification of Potential Core Genes in Parkinson’s Disease Using Bioinformatics Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Wei Quan ◽  
Jia Li ◽  
Xiya Jin ◽  
Li Liu ◽  
Qinghui Zhang ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Substantia Nigra ◽  
Metabolic Pathways ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Hub Genes ◽  
Potential Core ◽  
Core Genes

Purpose. This study aimed to explore new core genes related to the occurrence of Parkinson’s disease (PD) and core genes that can lead to the progression of PD. Methods. The expression profile data of GSE42966, which contained six substantia nigra tissues isolated from normal individuals and nine substantia nigra tissues isolated from patients with PD, were obtained from Gene Expression Omnibus. Differentially expressed genes (DEGs) were identified, followed by functional enrichment analysis and protein-protein interaction (PPI) network construction. We then identified 10 hub genes and analyzed their expression in different Braak stages. Results. A total of 773 DEGs were identified that were significantly enriched in metabolic pathways. Ten hub genes were identified through the PPI network, namely, GNG3, MAPK1, FPR1, ATP5B, GNG2, PRKACA, HRAS, HSPA8, PSAP, and GABBR2. The expression of HRAS was different in patients with PD with Braak stages 3 and 4. Conclusion. These 10 hub genes and the metabolic pathways they are enriched in may be involved in the pathogenesis of PD. HRAS may have potential value in predicting the progression of PD.

scholarly journals Identification of Potential miRNA-mRNA Regulatory Network Contributing to Parkinson’s Disease

2021 ◽  
Author(s):  
Xi Yin ◽  
Miao Wang ◽  
Wei Wang ◽  
Tong Chen ◽  
Ge Song ◽  
...  
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Regulatory Network ◽  
Enrichment Analysis ◽  
Functional Enrichment ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Rab Protein ◽  
Healthy Donors ◽  
Geo Database

Abstract Parkinson’s disease (PD) is a common neurodegenerative disease and the mechanism underlying PD pathogenesis is incompletely understood. Increasing evidence indicates that microRNA (miRNA) plays critical regulatory role in the pathogenesis of PD. This study aimed to determine the miRNA-mRNA regulatory network for PD. The differentially expressed miRNAs (DEmis) and genes (DEGs) between PD patients and healthy donors were screened from miRNA dataset GSE16658 and mRNA dataset GSE100054 downloaded from the Gene Expression Omnibus (GEO) database. Target genes of the DEmis were selected when predicted by 3 or 4 online databases and overlapped with DEGs from GSE100054. Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted by Database for Annotation, Visualization and Integrated Discovery (DAVID) and Metascape analytic tool. The correlation between the screened genes and PD was evaluated by the online tool Comparative Toxicogenomics Database (CTD). The protein-protein interactions (PPI) network was built by STRING platform. Finally, we testify the expression of members of the miRNA-mRNA regulatory network in the blood samples collected from PD patients and healthy donors by using qRT-PCR. 1505 upregulated and 1302 downregulated DEGs, 77 upregulated DEmis and 112 downregulated DEmis were preliminarily screened from GEO database. Through further functional enrichment analysis, 10 PD-related hub genes were selected, including RAC1, IRS2, LEPR, PPARGC1A, CAMKK2, RAB10, RAB13, RAB27B, RAB11A and JAK2, which were mainly involved in Rab protein signaling transduction, AMPK signaling pathway and signaling by Leptin. The miRNA-mRNA regulatory network was constructed with 10 hub genes and their interacting miRNAs overlapped with DEmis, including miR-30e-5p, miR-142-3p, miR-101-3p, miR-32-3p, miR-508-5p, miR-642a-5p, miR-19a-3p and miR-21-5p. Analysis on clinical samples verified significant upregulation of LEPR and downregulation of miR-101-3p in PD patients compared with healthy donors. In the study, the potential miRNA-mRNA regulatory network was constructed in PD, which may provide novel insight into pathogenesis and treatment of PD.

scholarly journals The Important Role of Perituberal Tissue in Epileptic Patients with Tuberous Sclerosis Complex by the Transcriptome Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shuqiang Li ◽  
Huijie Shao ◽  
Liansheng Chang
Keyword(s):  
Tuberous Sclerosis ◽  
Tuberous Sclerosis Complex ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
R Language ◽  
Protein Protein Interaction

Epilepsy is most common in patients with tuberous sclerosis complex (TSC). However, in addition to the challenging treatment, the pathogenesis of epilepsy is still controversial. To determine the transcriptome characteristics of perituberal tissue (PT) and clarify its role in the pathogenesis of epilepsy, GSE16969 was downloaded from the GEO database for further study by comprehensive bioinformatics analysis. Identification of differentially expressed genes (DEGs), functional enrichment analysis, construction of protein-protein interaction (PPI) network, and selection of Hub genes were performed using R language, Metascape, STRING, and Cytoscape, respectively. Comparing with cortical tuber (CT), 220 DEGs, including 95 upregulated and 125 downregulated genes, were identified in PT and mainly enriched in collagen-containing extracellular matrix and positive regulation of receptor-mediated endocytosis, as well as the pathways of ECM-receptor interaction and neuroactive ligand-receptor interaction. As for normal cortex (NC), 1549 DEGs, including 30 upregulated and 1519 downregulated genes, were identified and mainly enriched in presynapse, dendrite and axon, and also the pathways of dopaminergic synapse and oxytocin signaling pathway. In the PPI network, 4 hub modules were found between PT and CT, and top 5 hub modules were selected between PT and NC. C3, APLNR, ANXA2, CD44, CLU, CP, MCHR2, HTR1E, CTSG, APP, and GNG2 were identified as Hub genes, of which, C3, CD44, ANXA2, HTR1E, and APP were identified as Hub-BottleNeck genes. In conclusion, PT has the unique characteristics different from CT and NC in transcriptome and makes us further understand its importance in the TSC-associated epilepsy.

scholarly journals Screening of Hub Genes Associated with Pulmonary Arterial Hypertension by Integrated Bioinformatic Analysis

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Yu Zeng ◽  
Nanhong Li ◽  
Zhenzhen Zheng ◽  
Riken Chen ◽  
Min Peng ◽  
...  
Keyword(s):  
Pulmonary Arterial Hypertension ◽  
Arterial Hypertension ◽  
Drug Targets ◽  
Bioinformatic Analysis ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Network Analyses ◽  
Pulmonary Arterial

Background. Pulmonary arterial hypertension (PAH) is a disease or pathophysiological syndrome which has a low survival rate with abnormally elevated pulmonary artery pressure caused by known or unknown reasons. In addition, the pathogenesis of PAH is not fully understood. Therefore, it has become an urgent matter to search for clinical molecular markers of PAH, study the pathogenesis of PAH, and contribute to the development of new science-based PAH diagnosis and targeted treatment methods. Methods. In this study, the Gene Expression Omnibus (GEO) database was used to downloaded a microarray dataset about PAH, and the differentially expressed genes (DEGs) between PAH and normal control were screened out. Moreover, we performed the functional enrichment analyses and protein-protein interaction (PPI) network analyses of the DEGs. In addition, the prediction of miRNA and transcriptional factor (TF) of hub genes and construction miRNA-TF-hub gene network were performed. Besides, the ROC curve was used to evaluate the diagnostic value of hub genes. Finally, the potential drug targets for the 5 identified hub genes were screened out. Results. 69 DEGs were identified between PAH samples and normal samples. GO and KEGG pathway analyses revealed that these DEGs were mostly enriched in the inflammatory response and cytokine-cytokine receptor interaction, respectively. The miRNA-hub genes network was conducted subsequently with 131 miRNAs, 7 TFs, and 5 hub genes (CCL5, CXCL12, VCAM1, CXCR1, and SPP1) which screened out via constructing the PPI network. 17 drugs interacted with 5 hub genes were identified. Conclusions. Through bioinformatic analysis of microarray data sets, 5 hub genes (CCL5, CXCL12, VCAM1, CXCR1, and SPP1) were identified from DEGs between control samples and PAH samples. Studies showed that the five hub genes might play an important role in the development of PAH. These 5 hub genes might be potential biomarkers for diagnosis or targets for the treatment of PAH. In addition, our work also indicated that paying more attention on studies based on these 5 hub genes might help to understand the molecular mechanism of the development of PAH.

scholarly journals Identification of hub genes and pathways in psoriasis through bioinformatics and validation by RT-qPCR

2021 ◽  
Author(s):  
Ling Ai Zou ◽  
Qichao Jian
Keyword(s):  
Signaling Pathway ◽  
Quantitative Polymerase Chain Reaction ◽  
Cytokine Receptor ◽  
Receptor Interaction ◽  
Hacat Cells ◽  
Receptor Signaling ◽  
Ppi Network ◽  
Hub Genes ◽  
Network Analyses ◽  
Receptor Signaling Pathway

Abstract Background Although several studies have attempted to investigate the aetiology and mechanism of psoriasis, the precise molecular mechanism remains unclear. Our study aimed to identify the hub genes and associated pathways that promote its pathogenesis in psoriasis, which would be helpful for the discovery of diagnostic and therapeutic markers. Methods GSE30999, GSE34248, GSE41662, and GSE50790 datasets were extracted from the Gene Expression Omnibus (GEO) database. The GEO profiles were integrated to obtain differentially expressed genes (DEGs) using the affy package in R software, with |logFC|> 1.5 and adjusted P < 0.05. The DEGs were utilised for Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and protein-protein interaction (PPI) network analyses. Hub genes were identified using Cytoscape and enriched for analysis in www.bioinformatics.com.cn. These hub genes were validated in the four aforementioned datasets and M5-induced HaCaT cells using real-time quantitative polymerase chain reaction (RT-qPCR). Results A total of 359 DEGs were identified, which were mostly associated with responses to bacterium, defence responses to other organism, and antimicrobial humoral response. These DEGs were mostly enriched in the steroid hormone biosynthesis pathway, NOD-like receptor signaling pathway, and cytokine-cytokine receptor interaction. PPI network analysis indicated seven genes (CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3) as the probable hub genes of psoriasis; CXCL10 had a positive correlation with the other six hub genes. The chord plot results further supported the GO and KEGG analysis results of the 359 DEGs. Seven predicted hub genes were validated to be upregulated in four datasets and M5-induced HaCaT cells using RT-qPCR. Conclusions The pathogenesis of psoriasis may be associated with seven hub genes (CXCL1, ISG15, CXCL10, STAT1, OASL, IFIT1, and IFIT3) and pathways, such as the NOD-like receptor signaling pathway and cytokine-cytokine receptor interaction. These hub genes, especially CXCL10, can be used as potential biomarkers in psoriasis.

scholarly journals Identification of the pivotal role of SPP1 in kidney stone disease based on multiple bioinformatics analysis

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Sen-Yuan Hong ◽  
Qi-Dong Xia ◽  
Jin-Zhou Xu ◽  
Chen-Qian Liu ◽  
Jian-Xuan Sun ◽  
...  
Keyword(s):  
Kidney Stone ◽  
Enrichment Analysis ◽  
Stone Disease ◽  
Functional Enrichment ◽  
Pivotal Role ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes ◽  
Hub Gene ◽  
Kidney Stone Disease

Abstract Background Kidney stone disease (KSD) is a multifactorial disease involving both environmental and genetic factors, whose pathogenesis remains unclear. This study aims to explore the hub genes related to stone formation that could serve as potential therapeutic targets. Methods Based on the GSE73680 dataset with 62 samples, differentially expressed genes (DEGs) between Randall’s plaque (RP) tissues and normal tissues were screened and weighted gene co-expression network analysis (WGCNA) was applied to identify key modules associated with KSD. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed to explore the biological functions. The protein–protein interaction (PPI) network was constructed to identify hub genes. Meanwhile, CIBERSORT and ssGSEA analysis were used to estimate the infiltration level of the immune cells. The correlations between hub genes and immune infiltration levels were also investigated. Finally, the top hub gene was selected for further GSEA analysis. Results A total of 116 DEGs, including 73 up-regulated and 43 down-regulated genes, were screened in the dataset. The red module was identified as the key module correlated with KSD. 53 genes were obtained for functional enrichment analysis by taking the intersection of DEGs and genes in the red module. GO analysis showed that these genes were mainly involved in extracellular matrix organization (ECM) and extracellular structure organization, and others. KEGG analysis revealed that the pathways of aldosterone-regulated sodium reabsorption, cell adhesion molecules, arachidonic acid (AA) metabolism, and ECM-receptor interaction were enriched. Through PPI network construction, 30 hub genes were identified. CIBERSORT analysis revealed a significantly increased proportion of M0 macrophages, while ssGSEA revealed no significant differences. Among these hub genes, SPP1, LCN2, MMP7, MUC1, SCNN1A, CLU, SLP1, LAMC2, and CYSLTR2 were positively correlated with macrophages infiltration. GSEA analysis found that positive regulation of JNK activity was enriched in RP tissues with high SPP1 expression, while negative regulation of IL-1β production was enriched in the low-SPP1 subgroup. Conclusions There are 30 hub genes associated with KSD, among which SPP1 is the top hub gene with the most extensive links with other hub genes. SPP1 might play a pivotal role in the pathogenesis of KSD, which is expected to become a potential therapeutic target, while its interaction with macrophages in KSD needs further investigation.

scholarly journals Comprehensive Analysis of a circRNA-miRNA-mRNA Network to Reveal Potential Inflammation-Related Targets for Gastric Adenocarcinoma

2020 ◽  
Vol 2020 ◽  
pp. 1-15 ◽  
Author(s):  
YunXia Liu ◽  
YeFeng Xu ◽  
Feng Xiao ◽  
JianFeng Zhang ◽  
YiQing Wang ◽  
...  
Keyword(s):  
Regulatory Network ◽  
Gene Networks ◽  
Expression Profiles ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Genetic Alterations ◽  
Functional Enrichment ◽  
Receptor Interaction ◽  
Ppi Network ◽  
Hub Genes

Gastric cancer (GC) is the most common malignancy of the stomach. This study was aimed at elucidating the regulatory network of circRNA-miRNA-mRNA and identifying the precise inflammation-related targets in GC. The expression profiles of GSE83521, GSE78091, and GSE33651 were obtained from the GEO database. Interactions between miRNAs and circRNAs were investigated by the Circular RNA Interactome, and targets of miRNAs were predicted with miRTarBase. Then, a circRNA/miRNA/mRNA regulatory network was constructed. Also, functional enrichment analysis of selected differentially expressed genes (DEGs) was performed. The inflammation-/GC-related targets were collected in the GeneCards and GenLiP3 database, respectively. And a protein-protein interaction (PPI) network of DE mRNAs was constructed with STRING and Cytoscape to identify hub genes. The genetic alterations, neighboring gene networks, expression levels, and the poor prognosis of hub genes were investigated in cBioPortal, Oncomine, and Human Protein Atlas databases and Kaplan-Meier plotter, respectively. A total of 10 DE miRNAs and 33 DEGs were identified. The regulatory network contained 26 circRNAs, 10 miRNAs, and 1459 mRNAs. Functional enrichment analysis revealed that the selected 33 DEGs were involved in negative regulation of fat cell differentiation, response to wounding, extracellular matrix- (ECM-) receptor interaction, and regulation of cell growth pathways. THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were selected as inflammation-related hub genes of GC in the PPI network. The genetic alterations in these hub genes were related to amplification and missense mutations. Furthermore, the genes RYR2, ERBB2, PI3KCA, and HELZ2 were connected to hub genes in this study. The hub gene levels in clinical specimens were markedly upregulated in GC tissues and correlated with poor overall survival (OS). Our results suggest that THBS1, FN1, CALM1, COL4A1, CTGF, and IGFBP5 were associated with the pathogenesis of gastric carcinogenesis and may serve as biomarkers and inflammation-related targets for GC.

scholarly journals Putative second hit rare genetic variants in families with seemingly GBA-associated Parkinson’s disease

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Muhammad Aslam ◽  
Nirosiya Kandasamy ◽  
Anwar Ullah ◽  
Nagarajan Paramasivam ◽  
Mehmet Ali Öztürk ◽  
...  
Keyword(s):  
Risk Factors ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Genetic Variants ◽  
Rare Variants ◽  
Alpha Synuclein ◽  
Younger Age ◽  
Targeted Treatments ◽  
Genes Encoding ◽  
Rare Genetic Variants

AbstractRare variants in the beta-glucocerebrosidase gene (GBA1) are common genetic risk factors for alpha synucleinopathy, which often manifests clinically as GBA-associated Parkinson’s disease (GBA-PD). Clinically, GBA-PD closely mimics idiopathic PD, but it may present at a younger age and often aggregates in families. Most carriers of GBA variants are, however, asymptomatic. Moreover, symptomatic PD patients without GBA variant have been reported in families with seemingly GBA-PD. These observations obscure the link between GBA variants and PD pathogenesis and point towards a role for unidentified additional genetic and/or environmental risk factors or second hits in GBA-PD. In this study, we explored whether rare genetic variants may be additional risk factors for PD in two families segregating the PD-associated GBA1 variants c.115+1G>A (ClinVar ID: 93445) and p.L444P (ClinVar ID: 4288). Our analysis identified rare genetic variants of the HSP70 co-chaperone DnaJ homolog subfamily B member 6 (DNAJB6) and lysosomal protein prosaposin (PSAP) as additional factors possibly influencing PD risk in the two families. In comparison to the wild-type proteins, variant DNAJB6 and PSAP proteins show altered functions in the context of cellular alpha-synuclein homeostasis when expressed in reporter cells. Furthermore, the segregation pattern of the rare variants in the genes encoding DNAJB6 and PSAP indicated a possible association with PD in the respective families. The occurrence of second hits or additional PD cosegregating rare variants has important implications for genetic counseling in PD families with GBA1 variant carriers and for the selection of PD patients for GBA targeted treatments.

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