Genomic Insights Into Statin Therapy: Differential Expression Analysis of Key Genes

In this study, we utilized microarray profiles, specifically GSE71220 and GSE11393 obtained from the GEO database, which provide gene expression data from blood samples. Through a comparison of differentially expressed genes in both datasets, we successfully identified 11 key genes that exhibited differential expression in groups A and B, respectively. To gain insights into their functional roles, we performed gene ontology (GO) enrichment analysis using the "BiNGO" plugin in Cytoscape. This analysis revealed that these genes are primarily associated with primary metabolic processes. Notably, 8 genes, namely EIF2S3, GZMK, PIK3R1, RORA, SART3, TGM2, WTAP, and ABCG1, were found to be involved in these processes. To further explore the interactions and relationships among these key genes, we conducted protein-protein interaction analysis using the STRING database and co-expression network analysis using the GeneMANIA plugin in Cytoscape. The PPI analysis highlighted RORA, NR1D2, PIK3R1, CKAP4, and GZMK as central players within the network. To validate our findings, we examined the expression profiles of the key genes using the GSE86216 dataset, which comprises blood samples from individuals using statins. The results from this validation set largely corroborated our previous findings, with the exception of 3 genes: LAMP3, NR1D2, and PIK3R1, which exhibited different expression patterns. In conclusion, our study utilized microarray datasets to identify key genes that are influenced by statin treatments. The differential expression and functional analysis of these genes provide valuable insights into the mechanisms underlying the effects of statins.