Journal of Shandong University (Health Sciences) ›› 2024, Vol. 62 ›› Issue (1): 31-37.doi: 10.6040/j.issn.1671-7554.0.2023.0799

• Clinical Medicine • Previous Articles    

Predication and bioinformatics analysis of preeclampsia-related Siglec-6 core genes

QIN Jinjin, CAO Chenyuan, XING Jiejie, AN Yan, HUANG Yuxiang   

  1. Department of Obstetrics, Affiliated Hospital of Hebei University, Baoding 072550, Hebei, China
  • Published:2024-02-02

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Abstract: Objective To investigate the core pathogenesis genes of preeclampsia through high throughput bioinformatics analysis. Methods Two microarray datasets of preeclampsia coding genes from Gene Expression Omnibus(GEO)were selected for bioinformatics analysis. The datasets were homogenized and corrected with R language, and then the up-regulated and down-regulated differentially expressed genes were selected, which were then analyzed with gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes(KEGG)signal pathway enrichment, protein interaction network analysis and core gene calculation analysis to identify the most relevant pathogenesis genes and signal pathways of preeclampsia. Five placentas of preeclampsia patients and normal pregnant women were randomly selected for real-time quantitative PCR to verify the core genes. Results A total of 38 up-regulated and 20 down-regulated genes were screened from the microarray GSE43942 and GSE66273. After homogenization of all data, gene ontology analysis showed that biological function was enriched in positive regulation of follicle-stimulating hormone secretion, molecular composition was enriched in extracellular region, and cell component was enriched in hormone activity. KEGG signaling pathway was enriched in the Peptide hormone metabolism pathway. The protein interaction network showed that there were 58 points and 30 lines among all differentially expressed genes. The cytohubba analysis identified Siglec-6 as the core gene of preeclampsia. The real-time quantitative PCR showed that the expression of Siglec-6 in the placental tissue of preeclampsia pregnant women was 2.85 times that of normal pregnant women, which was consistent with the results of bioinformatics analysis. Conclusion Siglec-6 could be used as a potential diagnostic and therapeutic marker for preeclampsia.

Key words: Preeclampsia, High throughput bioinformatics analysis, Gene ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes signal pathway enrichment analysis, Protein interaction network analysis, Real-time quantitative PCR

CLC Number: 

  • R714.244
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