Dataset Information

Systems Level Analysis and Identification of Pathways and Key Genes Associated with Delirium.

ABSTRACT: Delirium is a complex pathophysiological process, and multiple contributing mechanisms have been identified. However, it is largely unclear how the genes associated with delirium contribute and which of them play key roles. In this study, the genes associated with delirium were retrieved from the Comparative Toxicogenomics Database (CTD) and integrated through a protein-protein interaction (PPI) network. Delirium-associated genes formed a highly interconnected PPI subnetwork, indicating a high tendency to interact and agglomerate. Using the Molecular Complex Detection (MCODE) algorithm, we identified the top two delirium-relevant network modules, M1 and M5, that have the most significant enrichments for the delirium-related gene sets. Functional enrichment analysis showed that genes related to neurotransmitter receptor activity were enriched in both modules. Moreover, analyses with genes located in human accelerated regions (HARs) provided evidence that HAR-Brain genes were overrepresented in the delirium-relevant network modules. We found that four of the HAR-Brain genes, namely APP, PLCB1, NPY, and HTR2A, in the M1 module were highly connected and appeared to exhibit hub properties, which might play vital roles in delirium development. Further understanding of the function of the identified modules and member genes could help to identify therapeutic intervention targets and diagnostic biomarkers for delirium.

SUBMITTER: Takahashi Y

PROVIDER: S-EPMC7590056 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Systems Level Analysis and Identification of Pathways and Key Genes Associated with Delirium.

Takahashi Yukiko Y Terada Tomoyoshi T Muto Yoshinori Y

Genes 20201019 10

Delirium is a complex pathophysiological process, and multiple contributing mechanisms have been identified. However, it is largely unclear how the genes associated with delirium contribute and which of them play key roles. In this study, the genes associated with delirium were retrieved from the Comparative Toxicogenomics Database (CTD) and integrated through a protein-protein interaction (PPI) network. Delirium-associated genes formed a highly interconnected PPI subnetwork, indicating a high t ...[more]

PMID: 33086708

Similar Datasets

Project description:Toxic liver injury causes necrosis and fibrosis, which may lead to cirrhosis and liver failure. Despite recent progress in understanding the mechanism of liver fibrosis, our knowledge of the molecular-level details of this disease is still incomplete. The elucidation of networks and pathways associated with liver fibrosis can provide insight into the underlying molecular mechanisms of the disease, as well as identify potential diagnostic or prognostic biomarkers. Towards this end, we analyzed rat gene expression data from a range of chemical exposures that produced observable periportal liver fibrosis as documented in DrugMatrix, a publicly available toxicogenomics database. We identified genes relevant to liver fibrosis using standard differential expression and co-expression analyses, and then used these genes in pathway enrichment and protein-protein interaction (PPI) network analyses. We identified a PPI network module associated with liver fibrosis that includes known liver fibrosis-relevant genes, such as tissue inhibitor of metalloproteinase-1, galectin-3, connective tissue growth factor, and lipocalin-2. We also identified several new genes, such as perilipin-3, legumain, and myocilin, which were associated with liver fibrosis. We further analyzed the expression pattern of the genes in the PPI network module across a wide range of 640 chemical exposure conditions in DrugMatrix and identified early indications of liver fibrosis for carbon tetrachloride and lipopolysaccharide exposures. Although it is well known that carbon tetrachloride and lipopolysaccharide can cause liver fibrosis, our network analysis was able to link these compounds to potential fibrotic damage before histopathological changes associated with liver fibrosis appeared. These results demonstrated that our approach is capable of identifying early-stage indicators of liver fibrosis and underscore its potential to aid in predictive toxicity, biomarker identification, and to generally identify disease-relevant pathways.

Project description:BackgroundPancreatic cancer is an aggressive cancer with dismal prognosis, urgently necessitating better biomarkers to improve therapeutic options and early diagnosis. Traditional approaches of biomarker detection that consider only one aspect of the biological continuum like gene expression alone are limited in their scope and lack robustness in identifying the key regulators of the disease. We have adopted a multidimensional approach involving the cross-talk between the omics spaces to identify key regulators of disease progression.MethodsMultidimensional domain-specific disease signatures were obtained using rank-based meta-analysis of individual omics profiles (mRNA, miRNA, DNA methylation) related to pancreatic ductal adenocarcinoma (PDAC). These domain-specific PDAC signatures were integrated to identify genes that were affected across multiple dimensions of omics space in PDAC (genes under multiple regulatory controls, GMCs). To further pin down the regulators of PDAC pathophysiology, a systems-level network was generated from knowledge-based interaction information applied to the above identified GMCs. Key regulators were identified from the GMC network based on network statistics and their functional importance was validated using gene set enrichment analysis and survival analysis.ResultsRank-based meta-analysis identified 5391 genes, 109 miRNAs and 2081 methylation-sites significantly differentially expressed in PDAC (false discovery rate ≤ 0.05). Bimodal integration of meta-analysis signatures revealed 1150 and 715 genes regulated by miRNAs and methylation, respectively. Further analysis identified 189 altered genes that are commonly regulated by miRNA and methylation, hence considered GMCs. Systems-level analysis of the scale-free GMCs network identified eight potential key regulator hubs, namely E2F3, HMGA2, RASA1, IRS1, NUAK1, ACTN1, SKI and DLL1, associated with important pathways driving cancer progression. Survival analysis on individual key regulators revealed that higher expression of IRS1 and DLL1 and lower expression of HMGA2, ACTN1 and SKI were associated with better survival probabilities.ConclusionsIt is evident from the results that our hierarchical systems-level multidimensional analysis approach has been successful in isolating the converging regulatory modules and associated key regulatory molecules that are potential biomarkers for pancreatic cancer progression.

Project description:Background: Endometriosis is a common gynecological disorder with high rates of infertility and pelvic pain. However, its pathogenesis and diagnostic biomarkers remain unclear. This study aimed to elucidate potential hub genes and key pathways associated with endometriosis in ectopic endometrium (EC) and eutopic endometrium (EU). Material and Method: EC and EU-associated microarray datasets were obtained from the gene expression omnibus (GEO) database. Gene set enrichment analysis was performed to obtain further biological insight into the EU and EC-associated genes. Weighted gene co-expression network analysis (WGCNA) was performed to find clinically significant modules of highly-correlated genes. The hub genes that belong to both the weighted gene co-expression network and protein-protein interaction (PPI) network were identified using a Venn diagram. Results: We obtained EC and EU-associated microarray datasets GSE7305 and GSE120103. Genes in the EC were mainly enriched in the immune response and immune cell trafficking, and genes in the EU were mainly enriched in stress response and steroid hormone biosynthesis. PPI networks and weighted gene co-expression networks were constructed. An EC-associated blue module and an EU-associated magenta module were identified, and their function annotations revealed that hormone receptor signaling or inflammatory microenvironments may promote EU passing through the oviducts and migrating to the ovarian surfaces, and adhesion and immune correlated genes may induce the successful ectopic implantation of the endometrium (EC). Twelve hub genes in the EC and sixteen hub genes in the EU were recognized and further validated in independent datasets. Conclusion: Our study identified, for the first time, the hub genes and enrichment pathways in the EC and EU using WGCNA, which may provide a comprehensive understanding of the pathogenesis of endometriosis and have important clinical implications for the treatment and diagnosis of endometriosis.

Dataset Information

Systems Level Analysis and Identification of Pathways and Key Genes Associated with Delirium.

Publications

Systems Level Analysis and Identification of Pathways and Key Genes Associated with Delirium.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets