Evaluation of genes and molecular pathways involved in the oxidative stress in preeclampsia: A systems biology approach

Background: Oxidative stress is one of the main factors in the pathogenesis of preeclampsia (PE). Many genes and molecular pathways play a role in its occurrence, however, so far no study has been conducted with a bioinformatics approach to identify genes. Therefore, we investigated this issue in this study.Material and methods: In this study, microarray data (GSE30186 dataset) sourced from the Gene Expression Omnibus (GEO) database were analyzed. This dataset consists of 12 placental tissue samples; six was obtained from normal individuals and six from patients diagnosed with preeclampsia. Differential gene analysis was conducted with the LIMMA package in R software using the following criteria 1: absolute log fold change >1, 2: adjusted P-value <0.05. Oxidative stress-related genes were sourced from Genecard, and the overlap with DEGs was extracted via a Venn diagram. Gene ontology and KEGG pathway analysis were performed using Enrichr software. A protein-protein interaction (PPI) network was designed using the STRING database and visualized with Cytoscape. Hub genes were identified using various methods. Associations between hub genes, transcription factors, and microRNAs were assessed using the miRTarBase and TRRUST databases. Hub gene validation was conducted on the GSE75010 dataset, which comprises 80 preeclampsia and 77 control placenta samples. Differential expression analysis was performed using a two-sample t-test (P < 0.05); boxplots were also generated for visualization.Result: Analysis of the GSE30186 dataset identified 717 DEGs in PE compared to normal samples with 448 upregulated and 269 downregulated DEGs. To investigate oxidative stress in PE, a list of 391 genes related to oxidative stress was initially collected from Genecard. Subsequently, an intersection was identified between these oxidative stress-related genes and DEGs, resulting in the identification of 26 shared genes between the DEGs and the oxidative stress-related genes. PPI network and hub gene analyses identified 8 hub genes, including STAT1, SERPINE1, CXCL8, and FN1. According to the results, hsa-miR-146a-5p has the most interactions with hub genes among miRNAs, and RELA has the most interactions among TFs. Finally, validation using the GSE75010 dataset confirmed significant differential expression of SERPINE1, CXCL8, and FN1 in PE; it suggested cited genes as hub genes, while STAT1 did not exhibit significant differences.Conclusion: Identifying pathways related to the expression of hub genes can be useful in designing appropriate treatment strategies. On the other hand, examining the pathways activated by hub genes can be effective in preventing ROS production.