Protective mechanism of TCF7L1 against retinal photoreceptor cell injury following retinitis pigmentosa based on the GEO database

Abstract

Recent studies have reported the promising value of differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) for identifying disease biomarkers. Based on this method, this study intends to characterize the hub genes and pathways related to retinal photoreceptor cell (PRC) injury in the context of retinitis pigmentosa (RP). A total of 53 coexpression modules were identified by WGCNA, among which lightpink4, darkolivegreen, tan4, blue2, skyblue2, and navajowhite2 ranked at the top. By analyzing the RP microarrays retrieved from the GEO database, 338 differentially expressed genes (DEGs) were identified in the RP samples. Forty-five candidate genes were selected from these DEGs by intersection with the genes in the coexpression modules. These intersection genes were subjected to GO and KEGG analyses. Furthermore, the genes and pathways involved in PRC damage were identified based on analyses utilizing GeneCards and STRING tools. Transcription factor 7-like 1 (TCF7L1, also called TCF3) was suggested to participate in the RP-associated PRC damage through the Wnt signaling pathway. It was validated in a blue light-irradiated cell model that TCF7L1 overexpression boosted PRC viability and repressed apoptosis. Inhibition of the Wnt signaling pathway also contributed to protective effects. Together, the data mentioned above supported the conclusion that either elevation of TCF7L1 or blockade of the Wnt signaling pathway could prevent RP progression by protecting PRCs from damage.

Anti‐NSCLC role of SCN4B by negative regulation of the cGMP‐PKG pathway: Integrated utilization of bioinformatics analysis and in vitro assay validation

“…The GEO database is used for effective searching and data mining, thereby identify entries of interest (Barrett et al, 2013). Recently, GEO database has been clearly demonstrated to be a great tool to identify differentially expressed genes (DEGs) in various diseases by bioinformatic analysis (Bai et al, 2022; Yin et al, 2023).…”