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Preeclampsia (PE) is a disorder of pregnancy that is characterised by hypertension and a significant amount of proteinuria beginning after 20 weeks of pregnancy. It is closely associated with high maternal morbidity, mortality, maternal organ dysfunction or foetal growth restriction. Therefore, it is necessary to identify early and novel diagnostic biomarkers of PE. In the present study, we performed a multi-step integrative bioinformatics analysis of microarray data for identifying hub genes as diagnostic biomarkers of PE. With the help of gene expression profiles of the Gene Expression Omnibus (GEO) dataset GSE60438, a total of 268 dysregulated genes were identified including 131 up- and 137 down-regulated differentially expressed genes (DEGs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of DEGs suggested that DEGs were significantly enriched in disease-related biological processes (BPs) such as hormone activity, immune response, steroid hormone biosynthesis, metabolic pathways, and other signalling pathways. Using the STRING database, we established a protein-protein interaction (PPI) network based on the above DEGs. Module analysis and identification of hub genes were performed to screen a total of 17 significant hub genes. The support vector machines (SVMs) model was used to predict the potential application of biomarkers in PE diagnosis with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.958 in the training set and 0.834 in the test set, suggesting that this risk classifier has good discrimination between PE patients and control samples. Our results demonstrated that these 17 differentially expressed hub genes can be used as potential biomarkers for diagnosis of PE.

An integrative bioinformatics analysis of microarray data for identifying hub genes as diagnostic biomarkers of preeclampsia