Bioinformatics analysis of differentially expressed genes in retinoblastoma
Journal Title: Eye Science (Yanke Xuebao) - Year 2021, Vol 36, Issue 5
Abstract
[Objective:] To find differentially expressed genes between retinoblastoma and normal retinal tissues by bioinformatics analysis, and to investigate their molecular function and interactions in the transcriptional level. [Methods:] The gene expression profile datasets GSE97508 and GSE110811, including 34 retinoblastoma (RB) tissues and 6 normal retinal tissues, were downloaded from gene expression omnibus (GEO). Differentially expressed genes (DEGs) between normal retinal tissues and RB tissues were identified by GEO2R tool and the Draw Venn Diagrams software. The gene ontology (GO) analysis, KEGG pathway and protein-protein interaction (PPI) were analyzed by STRING. [Results:] In two microarrays of retinoblastoma we found total 20 DEGs were identified, including 3 up-regulated and 17 down-regulated genes. The GO ontology annotation results showed that the enrichment functions of up-regulated genes were mainly in cell division, chromosome enrichment, nuclear division, and DNA conformation change. Down-regulated genes were mainly concentrated in light conduction, visual perception, photoreceptor cell repair, photoreceptor cell inner and outer segment, and regulation of rhodopsin mediated signaling pathway. The KEGG pathway showed that there was no significant signal pathway in which up-regulated genes up-regulated DEGs, and down-regulated genes were involved in the phototransduction signaling pathway, including four genes of CNGA1, CNGB1, RHO and SAG. PPI network suggested that these four genes were interlinked, and RHO was found to be the most closely connected core gene with other nodes. [Conclusion:] Bioinformatics can be used effectively to analyze RB microarray data to provide theoretical reference for further exploration of tumorigenesis mechanism and help search for potential drug therapeutic targets.
Authors and Affiliations
Yan ZHANG, Yuyu WU
Keywords
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- EP ID EP763506
- DOI 10.3978/j.issn.1000-4432.2021.02.02
- Views 7
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