scholarly journals Gene Set Enrichment Analysis of Interaction Networks Weighted by Node Centrality

2021 ◽  
Vol 12 ◽  
Author(s):  
Alessandra Zito ◽  
Marta Lualdi ◽  
Paola Granata ◽  
Dario Cocciadiferro ◽  
Antonio Novelli ◽  
...  
Keyword(s):  
Betweenness Centrality ◽  
Cellular Response ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Comparative Genomic ◽  
Expression Data ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Ppi Networks

Gene set enrichment analysis (GSEA) is a powerful tool to associate a disease phenotype to a group of genes/proteins. GSEA attributes a specific weight to each gene/protein in the input list that depends on a metric of choice, which is usually represented by quantitative expression data. However, expression data are not always available. Here, GSEA based on betweenness centrality of a protein–protein interaction (PPI) network is described and applied to two cases, where an expression metric is missing. First, personalized PPI networks were generated from genes displaying alterations (assessed by array comparative genomic hybridization and whole exome sequencing) in four probands bearing a 16p13.11 microdeletion in common and several other point variants. Patients showed disease phenotypes linked to neurodevelopment. All networks were assembled around a cluster of first interactors of altered genes with high betweenness centrality. All four clusters included genes known to be involved in neurodevelopmental disorders with different centrality. Moreover, the GSEA results pointed out to the evidence of “cell cycle” among enriched pathways. Second, a large interaction network obtained by merging proteomics studies on three neurodegenerative disorders was analyzed from the topological point of view. We observed that most central proteins are often linked to Parkinson’s disease. The selection of these proteins improved the specificity of GSEA, with “Metabolism of amino acids and derivatives” and “Cellular response to stress or external stimuli” as top-ranked enriched pathways. In conclusion, betweenness centrality revealed to be a suitable metric for GSEA. Thus, centrality-based GSEA represents an opportunity for precision medicine and network medicine.

scholarly journals Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13882 ◽  
Author(s):  
Binghuang Cai ◽  
Xia Jiang
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Gene Expression Data ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Expression Data ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Pathway Gene

Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results, in the form of pathway rankings, indicate that some pathways may behave abnormally in LUSC. For example, both the cell cycle and viral carcinogenesis pathways ranked very high in LUSC. Furthermore, some pathways that are known to be associated with cancer, such as the p53 and the PI3K-Akt signal transduction pathways, were found to rank high in LUSC. Other pathways, such as bladder cancer and thyroid cancer pathways, were also ranked high in LUSC.

Identification and Functional Enrichment Analysis of Differentially Expressed Genes in Osteoarthritis

2020 ◽  
Author(s):  
Chen Xu ◽  
Ling-bing Meng ◽  
Yu Xiao ◽  
Yong Qiu ◽  
Ying-jue Du ◽  
...  
Keyword(s):  
Hydrogen Peroxide ◽  
Differentially Expressed Genes ◽  
Cellular Response ◽  
Enrichment Analysis ◽  
Functional Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Functional Enrichment ◽  
Ppi Network ◽  
Gene Set Enrichment ◽  
Gene Set

Abstract Background Osteoarthritis (OA) is a chronic, progressive, inflammatory, degenerative disease, which has become an osteoarthropathy that seriously affects physical health and quality of life of elderly people. However, the etiology and pathogenesis of OA remains unclear. Therefore, the study purposed to utilize bioinformatics technology to perform identification and functional enrichment analysis of differentially expressed genes in osteoarthritis. Method The main methods of this study consist of access to microarray data (GSE82107 and GSE55235), identification of differently expressed genes (DEGs) by GEO2R between OA and normal synovium samples, enrichment analysis of Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) by Gene Set Enrichment Analysis (GSEA), construction and analysis of protein-protein interaction (PPI) network, significant module and hub genes. Result A total of 300 DEGs were identified, consisting of 64 up-regulated genes and 11 down-regulated genes in OA samples compared to normal synovium tissues. Gene set enrichment analysis of DEGs provided a comprehensive overview of some major pathophysiological mechanisms in OA: cellular response to hydrogen peroxide, P53 signaling pathway and so on. The study also built the PPI network, and a total of 10 key genes were identified: CYR61, PENK, GOLM1, DUSP1, ATF3, STC2, FOSB, PRSS23, TF, and TNC. Conclusion DEGs exists between OA patients and normal cartilage tissue, which may be involved in the related mechanism of OA development, especially cellular response to hydrogen peroxide and CYR61.

scholarly journals Towards a gold standard for benchmarking gene set enrichment analysis

2019 ◽  
Author(s):  
Ludwig Geistlinger ◽  
Gergely Csaba ◽  
Mara Santarelli ◽  
Marcel Ramos ◽  
Lucas Schiffer ◽  
...  
Keyword(s):  
Ad Hoc ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Rna Seq ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets ◽  
Enrichment Methods

AbstractBackgroundAlthough gene set enrichment analysis has become an integral part of high-throughput gene expression data analysis, the assessment of enrichment methods remains rudimentary and ad hoc. In the absence of suitable gold standards, evaluations are commonly restricted to selected data sets and biological reasoning on the relevance of resulting enriched gene sets. However, this is typically incomplete and biased towards the goals of individual investigations.ResultsWe present a general framework for standardized and structured benchmarking of enrichment methods based on defined criteria for applicability, gene set prioritization, and detection of relevant processes. This framework incorporates a curated compendium of 75 expression data sets investigating 42 different human diseases. The compendium features microarray and RNA-seq measurements, and each dataset is associated with a precompiled GO/KEGG relevance ranking for the corresponding disease under investigation. We perform a comprehensive assessment of 10 major enrichment methods on the benchmark compendium, identifying significant differences in (i) runtime and applicability to RNA-seq data, (ii) fraction of enriched gene sets depending on the type of null hypothesis tested, and (iii) recovery of the a priori defined relevance rankings. Based on these findings, we make practical recommendations on (i) how methods originally developed for microarray data can efficiently be applied to RNA-seq data, (ii) how to interpret results depending on the type of gene set test conducted, and (iii) which methods are best suited to effectively prioritize gene sets with high relevance for the phenotype investigated.ConclusionWe carried out a systematic assessment of existing enrichment methods, and identified best performing methods, but also general shortcomings in how gene set analysis is currently conducted. We provide a directly executable benchmark system for straightforward assessment of additional enrichment methods.Availabilityhttp://bioconductor.org/packages/GSEABenchmarkeR

scholarly journals A Gene Set Enrichment Analysis of multiomic celiac disease data

2017 ◽  
Author(s):  
Eugenio Del Prete ◽  
Angelo Facchiano ◽  
Pietro Liò
Keyword(s):  
Celiac Disease ◽  
Cellular Response ◽  
System Development ◽  
Enrichment Analysis ◽  
Point Of View ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Exact Test ◽  
Gene Set ◽  
Immune System Development

Celiac disease is a chronic condition, which can be described as inflammatory and autoimmune. The well-known treatment is a lifelong gluten-free diet, but it can be not totally effective for a high percentage of the patients. The aim of this work is to approach the celiac disease complexity from a bioinformatics point of view. The idea is to analyse the state of the art from GEO online repository and revisit the works, by integrating gene expression data and Gene Ontology (GO) terms. Gene Set Enrichment Analysis (GSEA) is a set of statistical methods to classify genes in groups, which are related to common biological function, chromosomal location or regulation. The work is developed in R environment. The packages are downloaded by the online repository Bioconductor. The studies are not standardized. In these circumstances, the candidate genes subset is chosen with a trade-off among all the scores, thus the creation of a GO graph eludes the Fishers exact test, keeping its biological importance to define process clusters. A little framework on the biological processes involved in each study on celiac disease is suggested: GSE11501, peptidyl-tyrosine phosphorylation, phosphatidylinositol 3-kinase signaling, and response to endoplasmic reticulum stress; GSE87629, mitosis regulation, microtubule cytoskeleton organisation, and protein destabilization; GSE72625, signaling pathway and cellular response about interferon-gamma; GSE61849a, immune response and immune system development; GSE61849b, protein phosphorylation, apoptotic process, and regulation of cell adhesion; GSE76168, cytokine mediate signaling pathways.

scholarly journals A Gene Set Enrichment Analysis of multiomic celiac disease data

2017 ◽  
Author(s):  
Eugenio Del Prete ◽  
Angelo Facchiano ◽  
Pietro Liò
Keyword(s):  
Celiac Disease ◽  
Cellular Response ◽  
System Development ◽  
Enrichment Analysis ◽  
Point Of View ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Exact Test ◽  
Gene Set ◽  
Immune System Development

Celiac disease is a chronic condition, which can be described as inflammatory and autoimmune. The well-known treatment is a lifelong gluten-free diet, but it can be not totally effective for a high percentage of the patients. The aim of this work is to approach the celiac disease complexity from a bioinformatics point of view. The idea is to analyse the state of the art from GEO online repository and revisit the works, by integrating gene expression data and Gene Ontology (GO) terms. Gene Set Enrichment Analysis (GSEA) is a set of statistical methods to classify genes in groups, which are related to common biological function, chromosomal location or regulation. The work is developed in R environment. The packages are downloaded by the online repository Bioconductor. The studies are not standardized. In these circumstances, the candidate genes subset is chosen with a trade-off among all the scores, thus the creation of a GO graph eludes the Fishers exact test, keeping its biological importance to define process clusters. A little framework on the biological processes involved in each study on celiac disease is suggested: GSE11501, peptidyl-tyrosine phosphorylation, phosphatidylinositol 3-kinase signaling, and response to endoplasmic reticulum stress; GSE87629, mitosis regulation, microtubule cytoskeleton organisation, and protein destabilization; GSE72625, signaling pathway and cellular response about interferon-gamma; GSE61849a, immune response and immune system development; GSE61849b, protein phosphorylation, apoptotic process, and regulation of cell adhesion; GSE76168, cytokine mediate signaling pathways.

scholarly journals Fast gene set enrichment analysis

2016 ◽  
Author(s):  
Gennady Korotkevich ◽  
Vladimir Sukhov ◽  
Alexey Sergushichev
Keyword(s):  
Gene Expression ◽  
Gene Expression Data ◽  
Polynomial Algorithm ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Biological Processes ◽  
Expression Data ◽  
Gene Set Enrichment ◽  
P Values ◽  
Gene Set

AbstractPreranked gene set enrichment analysis (GSEA) is a widely used method for interpretation of gene expression data in terms of biological processes. Here we present FGSEA method that is able to estimate arbitrarily low GSEA P-values with a higher accuracy and much faster compared to other implementations. We also present a polynomial algorithm to calculate GSEA P-values exactly, which we use to practically confirm the accuracy of the method.

scholarly journals Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets

BMC Genomics ◽  
2014 ◽  
Vol 15 (Suppl 1) ◽  
pp. S6 ◽  
Author(s):  
Yinglei Lai ◽  
Fanni Zhang ◽  
Tapan K Nayak ◽  
Reza Modarres ◽  
Norman H Lee ◽  
...  
Keyword(s):  
Large Scale ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Data Sets ◽  
Expression Data ◽  
Gene Set Enrichment ◽  
Gene Set

Identification of key mRNAs, miRNAs, and mRNA-miRNA network involved in papillary thyroid carcinoma

2020 ◽  
Vol 15 ◽  
Author(s):  
Wei Han ◽  
Dongchen Lu ◽  
Chonggao Wang ◽  
Mengdi Cui ◽  
Kai Lu
Keyword(s):  
Papillary Thyroid Carcinoma ◽  
Thyroid Carcinoma ◽  
Differential Expression Analysis ◽  
Interaction Network ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Integrated Analysis ◽  
Papillary Thyroid ◽  
Gene Set Enrichment ◽  
Mirna Expression Data

Background: In the past decades, the incidence of thyroid cancer (TC) has been gradually increasing, owing to the widespread use of ultrasound scanning devices. However, the key mRNAs, miRNAs, and mRNA-miRNA network in papillary thyroid carcinoma (PTC) has not been fully understood. Material and Methods: In this study, multiple bioinformatics methods were employed, including differential expression analysis, gene set enrichment analysis, and miRNA-mRNA interaction network construction. Results: First, we investigated the key miRNAs that regulated significantly more differentially expressed genes based on GSEA method. Second, we searched for the key miRNAs based on the mRNA-miRNA interaction subnetwork involved in PTC. We identified hsa-mir-1275, hsa-mir-1291, hsa-mir-206 and hsa-mir-375 as the key miRNAs involved in PTC pathogenesis. Conclusion: The integrated analysis of the gene and miRNA expression data not only identified key mRNAs, miRNAs, and mRNA-miRNA network involved in papillary thyroid carcinoma, but also improved our understanding of the pathogenesis of PTC.

scholarly journals Higher Acid-Base Imbalance Associated with Respiratory Failure Could Decrease the Survival of Patients with Scrub Typhus during Intensive Care Unit Stay: A Gene Set Enrichment Analysis

2019 ◽  
Vol 8 (10) ◽  
pp. 1580 ◽  
Author(s):  
Kyoung Min Moon ◽  
Kyueng-Whan Min ◽  
Mi-Hye Kim ◽  
Dong-Hoon Kim ◽  
Byoung Kwan Son ◽  
...  
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Respiratory Failure ◽  
Scrub Typhus ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Acid Base ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Gene Sets

Ninety percent of patients with scrub typhus (SC) with vasculitis-like syndrome recover after mild symptoms; however, 10% can suffer serious complications, such as acute respiratory failure (ARF) and admission to the intensive care unit (ICU). Predictors for the progression of SC have not yet been established, and conventional scoring systems for ICU patients are insufficient to predict severity. We aimed to identify simple and robust indicators to predict aggressive behaviors of SC. We evaluated 91 patients with SC and 81 non-SC patients who were admitted to the ICU, and 32 cases from the public functional genomics data repository for gene expression analysis. We analyzed the relationships between several predictors and clinicopathological characteristics in patients with SC. We performed gene set enrichment analysis (GSEA) to identify SC-specific gene sets. The acid-base imbalance (ABI), measured 24 h before serious complications, was higher in patients with SC than in non-SC patients. A high ABI was associated with an increased incidence of ARF, leading to mechanical ventilation and worse survival. GSEA revealed that SC correlated to gene sets reflecting inflammation/apoptotic response and airway inflammation. ABI can be used to indicate ARF in patients with SC and assist with early detection.

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