GPX3 and GSTT1 as biomarkers related to oxidative stress during renal ischemia reperfusion injuries and their relationship with immune infiltration - Pei, Jun; Tian, Xiaomao; Yu, Chengjun; Luo, Jin; Zhang, Jie; Hua, Yi; Wei, Guanghui (Frontiers in Immunology, 2023)
Background: Renal ischemia reperfusion injuries (IRIs) are very common in clinical diagnoses and treatments, which are a common cause of impaired renal functions. Therefore, we aimed to determine biomarkers associated with oxidative stress during renal IRIs and their relationship with immune cell infiltration. Method: A differential gene expression analysis was made based on the GSE148420 dataset from the GEO Database combined with 92 oxidative-stress (OS)-related genes. Then we identified differentially-expressed genes (DEOSGs) associated with oxidative stress, which were used for GO and a KEGG enrichment analysis. At the same time, we used PPI protein interaction networks and Lasso regression analysis to identify key genes. Single-gene enrichment analysis (GSEA) was used to further clarify the underlying biological functions of key genes. Cibersort was used to analyze the immune cell infiltration during renal IRI and the correlation of key genes with immune cells. we constructed a network of TF-Hub genes and miRNA-Hub genes. DGIDB was used to predict drugs and molecular compounds that might interact with the Hub genes. Results: A total of 5456 DEGs were measured in the renal IRI group, 2486 of which were upregulated and 2970 were down-regulated. Among them, we found 30 DEOSGs. The results of GO and KEGG enrichment analysis showed that these DEOSGs were mainly enriched in glutathione metabolism, the response to oxidative stress stimulation, the regulation of T cell activation. Through PPI and a LASSO regression analysis, a total of two Hub genes were identified, namely GPX3 and GSTT1. The results of GSEA showed that the Hub genes were related to oxidative stress pathways and immune-response-related signaling pathways. An immunoinfiltration correlation analysis showed that genes GPX3 and GSTT1 were significantly positively correlated with plasma cells and macrophage M0, while were negatively correlated with monocytes and macrophages M1 and M2. Using the Strust, Starbase and DGIDB database, we predicted that 81 TF, 49 miRNAs and 13 drug might interact with the Hub genes. Conclusion: Through a comprehensive analysis of gene expression, our findings may provide new potential biomarkers for the pathogenesis of renal IRIs and a reliable basis for its early diagnosis as well as treatment.