Bioinformatics identification of potential genes and pathways in preeclampsia based on functional gene set enrichment analyses
Affiliations: Department of Obstetrics, The First People's Hospital of Jining, Jining, Shandong 272000, P.R. China
- Published online on: July 8, 2019 https://doi.org/10.3892/etm.2019.7749
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Preeclampsia is a complication of pregnancy characterized by new‑onset hypertension and proteinuria of gestation, with serious consequences for mother and infant. Although a vast amount of research has been performed on the pathogenesis of preeclampsia, the underlying mechanisms of this multisystemic disease have remained to be fully elucidated. Data were retrieved from Gene Expression Omnibus database GSE40182 dataset. After data preprocessing, differentially expressed genes of placental cells cultured in vitro from preeclampsia and normal pregnancy were determined and subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis to identify the associated pathways. Furthermore, functional principal component analysis (FPCA) was used to calculate the corresponding F‑value of each gene. In order to further study the key signaling pathways of preeclampsia, an elastic‑net regression model and the Mann‑Whitney U (MWU) test were used to estimate the weight of the signaling pathways. Finally, a co‑expression network was generated and hub genes were identified based on the topological features. A total of 134 pathways with a role in preeclampsia were identified. The gene expression data of placenta cells cultured in vitro for different durations were determined and F‑values of genes were estimated using the FPCA model. The top 1,000 genes were identified as the differentially expressed genes and subjected to further analysis by elastic‑net regression and MWU test. Two key signaling pathways were different between the preeclampsia and control groups, namely hsa05142 Chagas disease and hsa05204 Chemical carcinogenesis. Among the genes involved in these two key pathways, 13 hub genes were identified from the co‑expression network. Clustering analysis demonstrated that depending on these hub genes, it was possible to divide the sample into four distinct groups based on different incubation time. The top 3 candidates were Toll‑like receptor 2 (TLR2), glutathione S‑transferase omega 1 (GSTO1) and mitogen‑activated protein kinase 13 (MAPK13). TLR2 and associated pathways are known to be closely associated with preeclampsia, indirectly demonstrating the applicability of the analytic process applied. However, the role of GSTO1 and MAPK13 in preeclampsia has remained poorly investigated, and elucidation thereof may be a worthwhile endeavor. The present study may provide a basis for exploring potential novel genes and pathways as therapeutic targets for preeclampsia.