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pmid:36018119
Bioinformatic analysis identifies potential key genes in the pathogenesis of age-related macular degeneration.
Zhiyue Wang · Xinyu Huang · Xiao Lv · Chao Chen · Shen Qu · Xiaoyu Ma · Li Zhang · Yanlong Bi
Indian journal of ophthalmology · 2022 doi:10.4103/ijo.IJO_3211_21
Paper
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Experiment
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Data
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Analysis
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Execution
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Complete file
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More studies focused on screening the genes, which may be correlated with the development of AMD. With advances in various technologies like multiple microarray datasets, researchers could identify differentially expressed genes (DEGs) more accurately. Exploring abnormal gene expression in disease status can help to understand pathophysiological changes in complex diseases. This study aims to identify the key genes and upstream regulators in AMD and reveal factors, especially genetic association, and the prognosis of the development of this disease.\nMethods: Data from expression profile GSE125564 and profile GSE29801 were obtained from the Gene Expression Omnibus (GEO) database. We analyzed DEGs using R software (version 3.6.3). Functional enrichment and PPI network analysis were performed using the R package and online database STRING (version 11.0).\nResults: We compared AMD with normal and found 68 up-regulated genes (URGs) and 25 down-regulated genes (DRGs). We also compared wet AMD with dry AMD and found 41 DRGs in dry AMD. Further work including PPI network analysis, GO classification, and KEGG analysis was done to find connections with AMD. The URGs were mainly enriched in the biological process such as DNA replication, nucleoplasm, extracellular exosome, and cadherin binding. Besides, DRGs were mainly enriched in these functions such as an integral component of membrane and formation of the blood-aqueous barrier (BAB).\nConclusion: This study implied that core genes might involve in the process of AMD. Our findings may contribute to revealing the pathogenesis, developing new biomarkers, and raising strategies of treatment for AMD.", "claims": [{"id": "claim-method", "statement": "We used the Limma package in R software (version 3.6.3) to analyze GSE125564 and GSE29801 aiming to identify DEGs. 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