Differentially expressed genes identification and bioinformatics analysis of venous blood in patients with mild preeclampsia
DOI:
https://doi.org/10.3126/ajms.v13i11.47553Keywords:
Mild preeclampsia; Differential genes; Venous blood; BioinformaticsAbstract
Background: Preeclampsia (PE) is a syndrome characterized by hypertension (systolic blood pressure ≥140 mmHg, or diastolic blood pressure ≥90 mmHg) and proteinuria that develops after 20 weeks of gestation. It is classified as mild PE and severe PE. Placental abruption, fetal growth restriction, and fetal death are common complications of PE, which is a serious threat to maternal and infant safety during pregnancy. Bioinformatics analysis can dig into the undiscovered biological information, and have a deeper understanding of the pathogenesis of diseases. At present, the pathogenesis of PE has not been fully studied. There is no information in literature on key gene screening and bioinformatics studies of mild PE.
Aims and Objectives: The aim of the study was to explore the differential genes screening and related biological process (BP) in venous blood of patients with mild PE.
Materials and Methods: GSE48424 dataset was downloaded from Gene Expression Omnibus database, differential genes were screened. Gene ontology (GO)|Kyoto Encyclopedia of Gene and Genomes (KEGG) enrichment analysis was completed. The protein-protein interaction (PPI) network of differential genes was mapped using STRING and Cytoscape. Identify key modules and genes involved in mild PE.
Results: A total of 433 up-regulated and 1242 down-regulated genes were obtained. GO function analysis showed that BP was mainly related to endosome organization, dephosphorylation, and endomembrane system organization. Cellular component is mainly related to promyelocytic leukemia body, nuclear membrane, and nuclear envelope. Molecular function is mainly related to ubiquitin-like protein transferase activity，phosphoric ester hydrolase activity, and phosphatase activity. KEGG showed that differential genes were enriched in sphingolipid, TNF, herpes simplex virus 1 infection, and pancreatic cancer pathway. The PPI network of differential genes was constructed to obtain 10 key genes involved in mild PE: SGMS1, SGPP1, ASAH1, PPAP2C, PPAP2B, PPP1R12A, WDR82, PPP2R2A, PPP4R2, and PPP4R1.
Conclusion: Using bioinformatics technology to identify the hub genes involved in mild PE can provide a favorable basis for further study of biological markers and pathogenesis of mild PE.
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