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Research Article: Identification of biomarkers for the diagnosis of type 2 diabetes mellitus with metabolic associated fatty liver disease by bioinformatics analysis and experimental validation

Date Published: 2025-01-28

Abstract:
Background correction, normalization, and gene symbol conversion were applied to the MAFLD dataset and the T2DM dataset (GSE95849 and GSE23343). The R package DESeq2 and limma was employed for differential gene analysis of GEO microarray data in the normal and disease groups (19). The thresholds for identifying differential genes were |logFC| >1 and P < 0.05 for the control versus MAFLD disease dataset, and |logFC| >0.5 and P < 0.05 for the normal versus diabetes disease groups. Subsequently, the ‘ggplot2’ and ‘pheatmap’ tools in R software were used to visualize the expression patterns of differentially expressed genes (DEGs) as volcano plots and heat maps, respectively.

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