scholarly journals A comparative study of topology-based pathway enrichment analysis methods

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Jing Ma ◽  
Ali Shojaie ◽  
George Michailidis
Keyword(s):  
Comparative Study ◽  
Differential Expression ◽  
Enrichment Analysis ◽  
Superior Performance ◽  
Systematic Evaluation ◽  
Pathway Enrichment Analysis ◽  
Data Set ◽  
Metabolomics Data ◽  
Expression Levels ◽  
Pathway Enrichment

Abstract Background Pathway enrichment extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples. Results The findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment. Conclusion The analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

scholarly journals A Comparative Study of Topology-based Pathway Enrichment Analysis Methods

2019 ◽  
Author(s):  
Jing Ma ◽  
Ali Shojaie ◽  
George Michailidis
Keyword(s):  
Comparative Study ◽  
Differential Expression ◽  
Enrichment Analysis ◽  
Superior Performance ◽  
Systematic Evaluation ◽  
Pathway Enrichment Analysis ◽  
Data Set ◽  
Metabolomics Data ◽  
Expression Levels ◽  
Pathway Enrichment

AbstractBackgroundPathway enrichment analysis is extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples.ResultsThe findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment.ConclusionThe analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

scholarly journals Comprehensive Gene Expression Analysis Detects Global Reduction of Proteasome Subunits in Schizophrenia

2020 ◽  
Author(s):  
Libi Hertzberg ◽  
Nicola Maggio ◽  
Inna Muler ◽  
Assif Yitzhaky ◽  
Michael Majer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Expression Analysis ◽  
Gene Expression Analysis ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Down Regulation ◽  
Pathway Enrichment ◽  
Main Challenge ◽  
Proteasome Subunits

Abstract Background The main challenge in the study of schizophrenia is its high heterogeneity. While it is generally accepted that there exist several biological mechanisms that may define distinct schizophrenia subtypes, they have not been identified yet. We performed comprehensive gene expression analysis to search for molecular signals that differentiate schizophrenia patients from healthy controls and examined whether an identified signal was concentrated in a subgroup of the patients. Methods Transcriptome sequencing of 14 superior temporal gyrus (STG) samples of subjects with schizophrenia and 15 matched controls from the Stanley Medical Research Institute (SMRI) was performed. Differential expression and pathway enrichment analysis results were compared to an independent cohort. Replicability was tested on 6 additional independent datasets. Results The 2 STG cohorts showed high replicability. Pathway enrichment analysis of the down-regulated genes pointed to proteasome-related pathways. Meta-analysis of differential expression identified down-regulation of 12 of 39 proteasome subunit genes in schizophrenia. The signal of proteasome subunits down-regulation was replicated in 6 additional datasets (overall 8 cohorts with 267 schizophrenia and 266 control samples, from 5 brain regions). The signal was concentrated in a subgroup of patients with schizophrenia. Conclusions We detected global down-regulation of proteasome subunits in a subgroup of patients with schizophrenia. We hypothesize that the down-regulation of proteasome subunits leads to proteasome dysfunction that causes accumulation of ubiquitinated proteins, which has been recently detected in a subgroup of schizophrenia patients. Thus, down-regulation of proteasome subunits might define a biological subtype of schizophrenia.

scholarly journals Proteomic signature of the Dravet syndrome in the genetic Scn1a-A1783V mouse model

2021 ◽  
Author(s):  
Nina Miljanovic ◽  
Stefanie M. Hauck ◽  
R. Maarten van Dijk ◽  
Valentina Di Liberto ◽  
Ali Rezaei ◽  
...  
Keyword(s):  
Mouse Model ◽  
Enrichment Analysis ◽  
Dravet Syndrome ◽  
Pathway Enrichment Analysis ◽  
Data Set ◽  
Pathophysiological Mechanisms ◽  
Nitric Oxide Signaling ◽  
Pathway Enrichment ◽  
Proteomic Data ◽  
Genetic Deficiency

AbstractBackgroundDravet syndrome is a rare, severe pediatric epileptic encephalopathy associated with intellectual and motor disabilities. Proteomic profiling in a mouse model of Dravet syndrome can provide information about the molecular consequences of the genetic deficiency and about pathophysiological mechanisms developing during the disease course.MethodsA knock-in mouse model of Dravet syndrome with Scn1a haploinsufficiency was used for whole proteome, seizure and behavioral analysis. Hippocampal tissue was dissected from two-(prior to epilepsy manifestation) and four-(following epilepsy manifestation) week-old male mice and analyzed using LC-MS/MS with label-free quantification. Proteomic data sets were subjected to bioinformatic analysis including pathway enrichment analysis. The differential expression of selected proteins was confirmed by immunohistochemical staining.ResultsThe findings confirmed an increased susceptibility to hyperthermia-associated seizures, the development of spontaneous seizures, and behavioral alterations in the novel Scn1a-A1873V mouse model of Dravet syndrome. As expected, proteomic analysis demonstrated more pronounced alterations following epilepsy manifestation. In particular, proteins involved in neurotransmitter dynamics, receptor and ion channel function, synaptic plasticity, astrogliosis, neoangiogenesis, and nitric oxide signaling showed a pronounced regulation in Dravet mice. Pathway enrichment analysis identified several significantly regulated pathways at the later time point, with pathways linked to synaptic transmission and glutamatergic signaling dominating the list.ConclusionIn conclusion, the whole proteome analysis in a mouse model of Dravet syndrome demonstrated complex molecular alterations in the hippocampus. Some of these alterations may have an impact on excitability or may serve a compensatory function, which, however, needs to be further confirmed by future investigations. The proteomic data indicate that, due to the molecular consequences of the genetic deficiency, the pathophysiological mechanisms may become more complex during the course of the disease. Resultantly, the management of Dravet syndrome may need to consider further molecular and cellular alterations. Ensuing functional follow-up studies, this data set may provide valuable guidance for the future development of novel therapeutic approaches.

scholarly journals An Integrated Analysis of Prognostic and Immune Infiltrates for Hub Genes as Potential Survival Indicators in Patients with Lung Adenocarcinoma

2021 ◽  
Author(s):  
Zhiyun Xu ◽  
Shi Wang ◽  
Zhijian Ren ◽  
Xiang Gao ◽  
Lin Xu ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Gene Ontology ◽  
Lung Adenocarcinoma ◽  
Immune Cells ◽  
Enrichment Analysis ◽  
Integrated Analysis ◽  
Pathway Enrichment Analysis ◽  
Hub Genes ◽  
Expression Levels ◽  
Pathway Enrichment

Abstract Objective: Lung adenocarcinoma is one of the major subtypes of lung cancer. However, the prognosis of individuals with LUAD is still not promising. Therefore, this research aims to discover useful biomarkers to enhance the treatment and diagnosis of LUAD.Methods: GEO2R was used to identify common up-regulated DEGs in the GSE32863, GSE40791 and GSE75037. The DEGs were submitted to Metascape for gene ontology and pathway enrichment analysis. Metascape was also utilized to construct the PPI network, and the MCODE plug-in was employed to filter important subnetworks. The prognosis and expression levels of the hub genes were evaluated using the UALCAN, GEPIA2, and Kaplan-Meier plotter databases. The Timer database was utilized to confirm the correlation between immune cells infiltration and the expression levels of hub genes in LUAD tissues.Results: This research discovered 307 common up-regulated DEGs, and gene ontology and pathway enrichment analysis indicated that they were mostly enriched in mitotic cell cycle process and cell cycle pathway. DEGs in the subnetwork with the largest number of genes were AURKB, CCNB2, CDC20, CDCA5, CDCA8, CENPF and KNTC1. The seven hub genes were highly expressed in LUAD tissues and had a poor prognosis. AURKB, CCNB2, and CDC20 were inversely associated with B and CD4+ T cells. CDCA5, CDCA8, and CENPF have a substantially negative correlation with B Cell, but positive correlation with Neutrophil. Conclusions: This research demonstrates that increased expression of seven hub genes is associated with worse prognosis for LUAD patients. Additionally, immune cells infiltrating LUAD tissues may serve as a regulating mechanism.

scholarly journals Integrated in silico MS-based phosphoproteomics and network enrichment analysis of RASopathy proteins

2021 ◽  
Vol 16 (1) ◽  
Author(s):  
Javier-Fernando Montero-Bullón ◽  
Óscar González-Velasco ◽  
María Isidoro-García ◽  
Jesus Lacal
Keyword(s):  
In Silico ◽  
Molecular Mechanisms ◽  
Mapk Pathway ◽  
Enrichment Analysis ◽  
Future Research ◽  
Pathway Enrichment Analysis ◽  
Data Set ◽  
Pathway Enrichment ◽  
Key Players ◽  
Hub Proteins

Abstract Background RASopathies are a group of syndromes showing clinical overlap caused by mutations in genes affecting the RAS-MAPK pathway. Consequent disruption on cellular signaling leads and is driven by phosphoproteome remodeling. However, we still lack a comprehensive picture of the different key players and altered downstream effectors. Methods An in silico interactome of RASopathy proteins was generated using pathway enrichment analysis/STRING tool, including identification of main hub proteins. We also integrated phosphoproteomic and immunoblotting studies using previous published information on RASopathy proteins and their neighbors in the context of RASopathy syndromes. Data from Phosphosite database (www.phosphosite.org) was collected in order to obtain the potential phosphosites subjected to regulation in the 27 causative RASopathy proteins. We compiled a dataset of dysregulated phosphosites in RASopathies, searched for commonalities between syndromes in harmonized data, and analyzed the role of phosphorylation in the syndromes by the identification of key players between the causative RASopathy proteins and the associated interactome. Results In this study, we provide a curated data set of 27 causative RASopathy genes, identify up to 511 protein–protein associations using pathway enrichment analysis/STRING tool, and identify 12 nodes as main hub proteins. We found that a large group of proteins contain tyrosine residues and their biological processes include but are not limited to the nervous system. Harmonizing published RASopathy phosphoproteomic and immunoblotting studies we identified a total of 147 phosphosites with increased phosphorylation, whereas 47 have reduced phosphorylation. The PKB signaling pathway is the most represented among the dysregulated phosphoproteins within the RASopathy proteins and their neighbors, followed by phosphoproteins implicated in the regulation of cell proliferation and the MAPK pathway. Conclusions This work illustrates the complex network underlying the RASopathies and the potential of phosphoproteomics for dissecting the molecular mechanisms in these syndromes. A combined study of associated genes, their interactome and phosphorylation events in RASopathies, elucidates key players and mechanisms to direct future research, diagnosis and therapeutic windows.

A Method of Pathway Enrichment Analysis Based Gene Expression Variability

2013 ◽  
Vol 40 (12) ◽  
pp. 1256
Author(s):  
XiaoDong JIA ◽  
XiuJie CHEN ◽  
Xin WU ◽  
JianKai XU ◽  
FuJian TAN ◽  
...  
Keyword(s):  
Gene Expression ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Expression Variability ◽  
Pathway Enrichment

Systematic Investigation of Quercetin for Treating Cardiovascular Disease Based on Network Pharmacology

2019 ◽  
Vol 22 (6) ◽  
pp. 411-420 ◽  
Author(s):  
Xian-Jun Wu ◽  
Xin-Bin Zhou ◽  
Chen Chen ◽  
Wei Mao
Keyword(s):  
Cardiovascular Disease ◽  
Cardiovascular Diseases ◽  
Signaling Pathway ◽  
Kegg Pathway ◽  
Enrichment Analysis ◽  
Network Pharmacology ◽  
Pathway Enrichment Analysis ◽  
Therapeutic Effects ◽  
Pathway Enrichment ◽  
Compound Target

Aim and Objective: Cardiovascular disease is a serious threat to human health because of its high mortality and morbidity rates. At present, there is no effective treatment. In Southeast Asia, traditional Chinese medicine is widely used in the treatment of cardiovascular diseases. Quercetin is a flavonoid extract of Ginkgo biloba leaves. Basic experiments and clinical studies have shown that quercetin has a significant effect on the treatment of cardiovascular diseases. However, its precise mechanism is still unclear. Therefore, it is necessary to exploit the network pharmacological potential effects of quercetin on cardiovascular disease. Materials and Methods: In the present study, a novel network pharmacology strategy based on pharmacokinetic filtering, target fishing, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, compound-target-pathway network structured was performed to explore the anti- cardiovascular disease mechanism of quercetin. Results:: The outcomes showed that quercetin possesses favorable pharmacokinetic profiles, which have interactions with 47 cardiovascular disease-related targets and 12 KEGG signaling pathways to provide potential synergistic therapeutic effects. Following the construction of Compound-Target-Pathway (C-T-P) network, and the network topological feature calculation, we obtained top 10 core genes in this network which were AKT1, IL1B, TNF, IL6, JUN, CCL2, FOS, VEGFA, CXCL8, and ICAM1. KEGG pathway enrichment analysis. These indicated that quercetin produced the therapeutic effects against cardiovascular disease by systemically and holistically regulating many signaling pathways, including Fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, MAPK signaling pathway, IL-17 signaling pathway and PI3K-Akt signaling pathway.

scholarly journals Signature RNAS and related regulatory roles in type 1 diabetes mellitus based on competing endogenous RNA regulatory network analysis

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Qinghong Shi ◽  
Hanxin Yao
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Network Analysis ◽  
Type 1 Diabetes Mellitus ◽  
Regulatory Network ◽  
Enrichment Analysis ◽  
Pathway Enrichment Analysis ◽  
Competing Endogenous Rna ◽  
Pathway Enrichment

Abstract Background Our study aimed to investigate signature RNAs and their potential roles in type 1 diabetes mellitus (T1DM) using a competing endogenous RNA regulatory network analysis. Methods Expression profiles of GSE55100, deposited from peripheral blood mononuclear cells of 12 T1DM patients and 10 normal controls, were downloaded from the Gene Expression Omnibus to uncover differentially expressed long non-coding RNAs (lncRNAs), mRNAs, and microRNAs (miRNAs). The ceRNA regulatory network was constructed, then functional and pathway enrichment analysis was conducted. AT1DM-related ceRNA regulatory network was established based on the Human microRNA Disease Database to carry out pathway enrichment analysis. Meanwhile, the T1DM-related pathways were retrieved from the Comparative Toxicogenomics Database (CTD). Results In total, 847 mRNAs, 41 lncRNAs, and 38 miRNAs were significantly differentially expressed. The ceRNA regulatory network consisted of 12 lncRNAs, 10 miRNAs, and 24 mRNAs. Two miRNAs (hsa-miR-181a and hsa-miR-1275) were screened as T1DM-related miRNAs to build the T1DM-related ceRNA regulatory network, in which genes were considerably enriched in seven pathways. Moreover, three overlapping pathways, including the phosphatidylinositol signaling system (involving PIP4K2A, INPP4A, PIP4K2C, and CALM1); dopaminergic synapse (involving CALM1 and PPP2R5C); and the insulin signaling pathway (involving CBLB and CALM1) were revealed by comparing with T1DM-related pathways in the CTD, which involved four lncRNAs (LINC01278, TRG-AS1, MIAT, and GAS5-AS1). Conclusion The identified signature RNAs may serve as important regulators in the pathogenesis of T1DM.

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