• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.4
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• pp.19-26
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• 2008

Gene set enrichment analysis (GSEA) is a computational method to identify statistically significant gene sets showing significant differences between two groups of microarray expression profiles and simultaneously uncover their biological meanings in an elegant way by employing gene annotation databases, such as Cytogenetic Band, KEGG pathways, gene ontology, and etc. For the gone set enrichment analysis, all the genes in a given dataset are first ordered by the signal-to-noise ratio between the groups and then further analyses are proceeded. Despite of its impressive results in several previous studies, however, gene ranking by the signal-to-noise ratio makes it difficult to consider highly up-regulated genes and highly down-regulated genes at the same time as the candidates of significant genes, which possibly reflect certain situations incurred in metabolic and signaling pathways. To deal with this problem, in this article, we investigate the gene set enrichment analysis method with Fisher criterion for gene ranking and also evaluate its effects in Leukemia related pathway analyses.

• Genomics & Informatics
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• v.5 no.3
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• pp.133-136
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• 2007

The Gene Set network viewer (GSnet) visualizes the functional enrichment of a given gene set with a protein interaction network and is implemented as a plug-in for the Cytoscape platform. The functional enrichment of a given gene set is calculated using a hypergeometric test based on the Gene Ontology annotation. The protein interaction network is estimated using public data. Set operations allow a complex protein interaction network to be decomposed into a functionally-enriched module of interest. GSnet provides a new framework for gene set analysis by integrating a priori knowledge of a biological network with functional enrichment analysis.

• Molecules and Cells
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• v.42 no.8
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• pp.579-588
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• 2019

Gene set enrichment analysis (GSEA) is a popular tool to identify underlying biological processes in clinical samples using their gene expression phenotypes. GSEA measures the enrichment of annotated gene sets that represent biological processes for differentially expressed genes (DEGs) in clinical samples. GSEA may be suboptimal for functional gene sets; however, because DEGs from the expression dataset may not be functional genes per se but dysregulated genes perturbed by bona fide functional genes. To overcome this shortcoming, we developed network-based GSEA (NGSEA), which measures the enrichment score of functional gene sets using the expression difference of not only individual genes but also their neighbors in the functional network. We found that NGSEA outperformed GSEA in identifying pathway gene sets for matched gene expression phenotypes. We also observed that NGSEA substantially improved the ability to retrieve known anti-cancer drugs from patient-derived gene expression data using drug-target gene sets compared with another method, Connectivity Map. We also repurposed FDA-approved drugs using NGSEA and experimentally validated budesonide as a chemical with anti-cancer effects for colorectal cancer. We, therefore, expect that NGSEA will facilitate both pathway interpretation of gene expression phenotypes and anti-cancer drug repositioning. NGSEA is freely available at www.inetbio.org/ngsea.

• Biomolecules & Therapeutics
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• v.30 no.1
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• pp.98-116
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• 2022

The small GTPase RhoA has been studied extensively for its role in actin dynamics. In this study, multiple bioinformatics tools were applied cooperatively to the microarray dataset GSE64714 to explore previously unidentified functions of RhoA. Comparative gene expression analysis revealed 545 differentially expressed genes in RhoA-null cells versus controls. Gene set enrichment analysis (GSEA) was conducted with three gene set collections: (1) the hallmark, (2) the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and (3) the Gene Ontology Biological Process. GSEA results showed that RhoA is related strongly to diverse pathways: cell cycle/growth, DNA repair, metabolism, keratinization, response to fungus, and vesicular transport. These functions were verified by heatmap analysis, KEGG pathway diagramming, and direct acyclic graphing. The use of multiple gene set collections restricted the leakage of information extracted. However, gene sets from individual collections are heterogenous in gene element composition, number, and the contextual meaning embraced in names. Indeed, there was a limit to deriving functions with high accuracy and reliability simply from gene set names. The comparison of multiple gene set collections showed that although the gene sets had similar names, the gene elements were extremely heterogeneous. Thus, the type of collection chosen and the analytical context influence the interpretation of GSEA results. Nonetheless, the analyses of multiple collections made it possible to derive robust and consistent function identifications. This study confirmed several well-described roles of RhoA and revealed less explored functions, suggesting future research directions.

• Genomics & Informatics
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• v.21 no.4
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• pp.45.1-45.11
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• 2023

Nonalcoholic fatty liver disease (NAFLD) is a common type of chronic liver disease, with severity levels ranging from nonalcoholic fatty liver to nonalcoholic steatohepatitis (NASH). The extent of liver fibrosis indicates the severity of NASH and the risk of liver cancer. However, the mechanism underlying NASH development, which is important for early screening and intervention, remains unclear. Weighted gene co-expression network analysis (WGCNA) is a useful method for identifying hub genes and screening specific targets for diseases. In this study, we utilized an mRNA dataset of the liver tissues of patients with NASH and conducted WGCNA for various stages of liver fibrosis. Subsequently, we employed two additional mRNA datasets for validation purposes. Gene set enrichment analysis (GSEA) was conducted to analyze gene function enrichment. Through WGCNA and subsequent analyses, complemented by validation using two additional datasets, we identified five genes (BICC1, C7, EFEMP1, LUM, and STMN2) as hub genes. GSEA analysis indicated that gene sets associated with liver metabolism and cholesterol homeostasis were uniformly downregulated. BICC1, C7, EFEMP1, LUM, and STMN2 were identified as hub genes of NASH, and were all related to liver metabolism, NAFLD, NASH, and related diseases. These hub genes might serve as potential targets for the early screening and treatment of NASH.

• Genomics & Informatics
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• v.15 no.2
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• pp.56-64
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• 2017

We have previously reported that NS-398, a cyclooxygenase-2 (COX-2)-selective inhibitor, inhibited replicative cellular senescence in human dermal fibroblasts and skin aging in hairless mice. In contrast, celecoxib, another COX-2-selective inhibitor, and aspirin, a non-selective COX inhibitor, accelerated the senescence and aging. To figure out causal factors for the senescence-modulating effect of the inhibitors, we here performed cDNA microarray experiment and subsequent Gene Set Enrichment Analysis. The data showed that several senescence-related gene sets were regulated by the inhibitor treatment. NS-398 up-regulated gene sets involved in the tumor necrosis factor ${\beta}$ receptor pathway and the fructose and mannose metabolism, whereas it down-regulated a gene set involved in protein secretion. Celecoxib up-regulated gene sets involved in G2M checkpoint and E2F targets. Aspirin up-regulated the gene set involved in protein secretion, and down-regulated gene sets involved in RNA transcription. These results suggest that COX inhibitors modulate cellular senescence by different mechanisms and will provide useful information to understand senescence-modulating mechanisms of COX inhibitors.

• Communications for Statistical Applications and Methods
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• v.16 no.3
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• pp.397-408
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• 2009

The development of microarray technology makes possible to analyse many thousands of genes simultaneously. While it is important to test each gene whether it shows changes in expression associated with a phenotype, human diseases are thought to occur through the interactions of multiple genes within a same functional cafe-gory. Recent research interests aims to directly test the behavior of sets of functionally related genes, instead of focusing on single genes. Gene set enrichment analysis(GSEA), significance analysis of microarray to gene-set analysis(SAM-GS) and parametric analysis of gene set enrichment(PAGE) have been applied widely as a tool for gene-set analyses. We describe their problems and propose an alternative method using a parametric analysis by adopting normal score transformation of gene expression values. Performance of the newly derived method is compared with previous methods on three real microarray datasets.

• BMB Reports
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• v.45 no.2
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• pp.120-125
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• 2012

We have developed a biologist-friendly, Java GUI application (GoBean) for GO term enrichment analysis. It was designed to be a comprehensive and flexible GUI tool for GO term enrichment analysis, combining the merits of other programs and incorporating extensive graphic exploration of enrichment results. An intuitive user interface with multiple panels allows for extensive visual scrutiny of analysis results. The program includes many essential and useful features, such as enrichment analysis algorithms, multiple test correction methods, and versatile filtering of enriched GO terms for more focused analyses. A unique graphic interface reflecting the GO tree structure was devised to facilitate comparisons of multiple GO analysis results, which can provide valuable insights for biological interpretation. Additional features to enhance user convenience include built in ID conversion, evidence code-based gene-GO association filtering, set operations of gene lists and enriched GO terms, and user -provided data files. It is available at http://neon.gachon.ac.kr/GoBean/.

• Restorative Dentistry and Endodontics
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• v.37 no.3
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• pp.142-148
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• 2012

Objectives: We analyzed gene-expression profiles after 14 day odontogenic induction of human dental pulp cells (DPCs) using a DNA microarray and sought candidate genes possibly associated with mineralization. Materials and Methods: Induced human dental pulp cells were obtained by culturing DPCs in odontogenic induction medium (OM) for 14 day. Cells exposed to normal culture medium were used as controls. Total RNA was extracted from cells and analyzed by microarray analysis and the key results were confirmed selectively by reverse-transcriptase polymerase chain reaction (RT-PCR). We also performed a gene set enrichment analysis (GSEA) of the microarray data. Results: Six hundred and five genes among the 47,320 probes on the BeadChip differed by a factor of more than two-fold in the induced cells. Of these, 217 genes were upregulated, and 388 were down-regulated. GSEA revealed that in the induced cells, genes implicated in Apoptosis and Signaling by wingless MMTV integration (Wnt) were significantly upregulated. Conclusions: Genes implicated in Apoptosis and Signaling by Wnt are highly connected to the differentiation of dental pulp cells into odontoblast.

• IMMUNE NETWORK
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• v.11 no.6
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• pp.406-411
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• 2011

Background: Invariant Natural killer T (iNKT) cells, a distinct subset of CD1d-restricted T cells with invariant $V{\alpha}{\beta}$ TCR, functionally bridge innate and adaptive immunity. While iNKT cells share features with conventional T cells in some functional aspects, they simultaneously produce large amount of Th1 and Th2 cytokines upon T-cell receptor (TCR) ligation. However, gene expression pattern in two types of cells has not been well characterized. Methods: we performed comparative microarray analyses of gene expression in murine iNKT cells and conventional $CD4^+CD25^-$ ${\gamma}{\delta}TCR^-$ T cells by using Gene Set Enrichment Analysis (GSEA) method. Results: Here, we describe profound differences in gene expression pattern between iNKT cells and conventional $CD4^+CD25^-$ ${\gamma}{\delta}TCR^-$ T cells. Conclusion: Our results provide new insights into the functional competence of iNKT cells and a better understanding of their various roles during immune responses.