Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

Zhou, Yingyao; Zhou, Bin; Pache, Lars; Chang, Max W.; Khodabakhshi, Alireza Hadj; Tanaseichuk, Olga; Benner, Chris; Chanda, Sumit K.

doi:10.1038/s41467-019-09234-6

Cited by 9,406 publications

(8,401 citation statements)

References 57 publications

Supporting

Mentioning

7,679

Contrasting

Unclassified

Order By: Relevance

“…based on the TCGA data sets for data collection and analysis were developed. Through cross‐validation of the databases combined with experimental verification, researchers can more efficiently confirm the reliability of the hub genes 14–17 . In this study, we aimed to identify CRC‐related hub genes’ differential expressions between CRC tissues and CRC paracancerous tissues.…”

Section: Introductionmentioning

confidence: 99%

“…Through crossvalidation of the databases combined with experimental verification, researchers can more efficiently confirm the reliability of the hub genes. [14][15][16][17] In this study, we aimed to identify CRC-related hub genes' differential expressions between CRC tissues and CRC paracancerous tissues. The Gene Ontology (GO) project, Gene Set Enrichment Analysis (GSEA) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis were applied to uncover the underlying biological functions and relationships of the identified key genes in CRC.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Identification of potential hub genes via bioinformatics analysis combined with experimental verification in colorectal cancer

Zhou

Yang

Yue

et al. 2020

Molecular Carcinogenesis

View full text Add to dashboard Cite

Colorectal cancer (CRC) is a kind of malignant cancer with high morbidity and mortality. The purpose of this study was to explore potential regulated key genes involved in CRC through bioinformatics analysis and experimental verification. The gene expression profile data were downloaded from the Gene Expression Omnibus, and the differential expression genes were detected in cancerous and paracancerous samples of CRC patients, respectively. Then functional enrichment analysis, such as the Kyoto Encyclopedia of Genes and Genomes pathway analysis as well as the protein‐protein interaction network were constructed, and the highly related genes were clustered by Molecular COmplex DEtection algorithm to find out the core interaction in different genes' crosstalk. The genes affecting CRC prognosis were screened by the Human Protein Atlas database. In addition, the expression level of core genes was detected by GEPIA database, and the core genes' changes in large‐scale cancer genome data set were directly analyzed by cBioPortal database. The expression of the predicted hub genes DSN1, AHCY, and ERCC6L was verified by reverse‐transcription quantitative polymerase chain reaction in CRC cells. The gene function of DSN1 was analyzed by wound healing and colony formation assays. The results showed that silencing of DSN1 could significantly reduce the migration and proliferation of CRC cells. Further, BUB1B, the potential interacting protein of DSN1, was also predicted via bioinformatics analysis. Above all, this study shows that bioinformatics analysis combined with experimental method verification provide more potential vital genes for the prevention and therapy of CRC.

show abstract

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Identification of potential hub genes via bioinformatics analysis combined with experimental verification in colorectal cancer

Zhou

Yang

Yue

et al. 2020

Molecular Carcinogenesis

View full text Add to dashboard Cite

show abstract

“…First, the ASD risk gene list was downloaded from the Simons Foundation and Autism Research Initiative (SFARI) GENE database (https://gene.sfari.org/) to carry out the enrichment analyses by hypergeometric distribution test. Subsequently, the functional enrichment analyses of the DEGs, including Gene Ontology (GO) function analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, were carried out using annotation, visualization, and integrated discovery tools in the Metascape database (http://metascape.org) [Zhou et al, ] the GO analyses, cellular component (CC), biological process (BP), and molecular function (MF) terms were analyzed, and P ‐value <0.001 and gene counts ≥3 were used as statistically significant cutoffs. For the KEGG pathways analyses, enriched pathways were identified according to the hypergeometric distribution with a P ‐value <0.01 and gene counts ≥3.…”

Section: Methodsmentioning

confidence: 99%

Integrated Transcriptome Analyses Revealed Key Target Genes in Mouse Models of Autism

et al. 2019

View full text Add to dashboard Cite

Genetic mutations are the major pathogenic factor of Autism Spectrum Disorder (ASD). In recent years, more and more ASD risk genes have been revealed, among which there are a group of transcriptional regulators. Considering the similarity of the core clinical phenotypes, it is possible that these different factors may regulate the expression levels of certain key targets. Identification of these targets could facilitate the understanding of the etiology and developing of novel diagnostic and therapeutic methods. Therefore, we performed integrated transcriptome analyses of RNA‐Seq and microarray data in multiple ASD mouse models and identified a number of common downstream genes in various brain regions, many of which are related to the structure and function of the synapse components or drug addiction. We then established protein–protein interaction networks of the overlapped targets and isolated the hub genes by 11 algorithms based on the topological structure of the networks, including Sdc4, Vegfa, and Cp in the Cortex‐Adult subgroup, Gria1 in the Cortex‐Juvenile subgroup, and Kdr, S1pr1, Ubc, Grm2, Grin2b, Nrxn1, Pdyn, Grin3a, Itgam, Grin2a, Gabra2, and Camk4 in the Hippocampus‐Adult subgroup, many of which have been associated with ASD in previous studies. Finally, we cross compared our results with human brain transcriptional data sets and verified several key candidates, which may play important role in the pathology process of ASD, including SDC4, CP, S1PR1, UBC, PDYN, GRIN2A, GABRA2, and CAMK4. In summary, by integrated bioinformatics analysis, we have identified a series of potentially important molecules for future ASD research. Autism Res 2020, 13: 352–368. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Abnormal transcriptional regulation accounts for a significant portion of Autism Spectrum Disorder. In this study, we performed transcriptome analyses of mouse models to identify common downstream targets of transcriptional regulators involved in ASD. We identified several recurrent target genes that are close related to the common pathological process of ASD, including SDC4, CP, S1PR1, UBC, PDYN, GRM2, NRXN1, GRIN3A, ITGAM, GRIN2A, GABRA2, and CAMK4. These results provide potentially important targets for understanding the molecular mechanism of ASD.

show abstract

“…Metascape (http://metascape.org) was applied for GO analysis [33]. Metascape incorporates a core set of default ontologies including GO processes, KEGG pathways, Reactome gene sets, canonical pathways, and CORUM complexes for enrichment analysis.…”

Section: Analysis (Gsea)mentioning

confidence: 99%

Highly expressed miR-375 is not an intracellular oncogene in Merkel cell polyomavirus-associated Merkel cell carcinoma

Fan

Zebisch

Horny

et al. 2019

Preprint

View full text Add to dashboard Cite

201-1836945 2 AbstractPurpose. miR-375 is a highly abundant miRNAs in Merkel cell carcinoma. miR-375 may act as a tumor suppressor or oncogene depending on the cell context. While miR-375 is present as circulating free in the serum of patients with advanced MCC and thus serves as surrogate marker for tumor burden, its function within MCC has not been established.Methods. miR-375 knockdown was performed using miR-375 antagomiRs via lipofectamine transfection or nucleofection in classical MCC cell lines WaGa and PeTa. Viability and both changes in growth characteristics as well as morphology were determined. Genes targeted by miR-375 were predicted using ENCORI; based on these miR-375 regulated signaling pathways were determined by genes ontology (GO) and gene set enrichment analysis (GSEA).Expression of these genes was analyzed by multiplexed RT-qPCR to check the effect of miR-375 knockdown on these signaling pathways.Results. Complete knockdown of miR-375 expression by antagomiRs was only achieved by using nucleofection. This knockdown did not affect cell growth pattern, morphology, or proliferative capacity. miR-375 predicted target genes GO analysis revealed that Hippo signaling and focal adhesion-related genes were likely to be regulated by miR-375. GSEA of gene expression data of MCC cell lines further strengthened the regulation of Focal adhesionrelated genes by miR-375. However, gene expression analysis revealed that miR-375 knockdown had only a limited effect on expression of these pathways related genes. Conclusions.Complete miR-375 knockdown did neither change cell viability, morphology, nor oncogenic signaling pathways; these observations render miR-375 unlikely as intracellular oncogene in MCC cells.

show abstract

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

Cited by 9,406 publications

References 57 publications

Identification of potential hub genes via bioinformatics analysis combined with experimental verification in colorectal cancer

Identification of potential hub genes via bioinformatics analysis combined with experimental verification in colorectal cancer

Integrated Transcriptome Analyses Revealed Key Target Genes in Mouse Models of Autism

Highly expressed miR-375 is not an intracellular oncogene in Merkel cell polyomavirus-associated Merkel cell carcinoma

Contact Info

Product

Resources

About