Gene Expression Network Analysis Identifies Potential Targets for Prevention of Preeclampsia.

OBJECTIVE

Preeclampsia (PE) is a pregnancy-specific multisystem disease as well as an important cause of maternal and perinatal death. This study aimed to analyze the placental transcriptional data and clinical information of PE patients available in the published database and predict the target genes for prevention of PE.

METHODS

The clinical information and corresponding RNA data of PE patients were downloaded from the GEO database. Cluster analysis was performed to examine the correlation between different genotyping genes and clinical manifestations. Then, bioinformatic approaches including GO, KEGG, WGCNA, and GSEA were employed to functionally characterize candidate target genes involved in pathogenesis of PE.

RESULTS

Two PE datasets GSE60438 and GSE75010 were obtained and combined, thereby providing the data of 205 samples in total (100 non-PE and 105 PE samples). After eliminating the batch effect, we grouped and analyzed the integrated data, and further performed GSEA analysis. It was found that the genes in group 1 and group 2 were different from those in normal samples. Moreover, WGCNA analysis revealed that genes in group 1 were up-regulated in turquoise module, including , and , while genes in group 2 were up-regulated in the blue and brown modules. We further conducted GO and KEGG pathway enrichment analyses and found that the differential genes in turquoise module were mainly involved in biological processes such as small molecular catabolic process, while being highly enriched in pathways, including MAPK signaling pathway and Rap1 signaling pathway.

CONCLUSION

FLT-1 was conventionally used to predict PE risk, and sFLT-1 could also be used as an indicator to evaluate PE treatment effect. As a candidate biomarker for predicting PE, SASH1 may participate in proliferation, migration, invasion and epithelial mesenchymal transformation of human trophoblast cells by regulating MAPK pathway and Rap1 signaling pathway, thus affecting the progression of PE. The mechanism allowing PIK3CB to regulate PE development was not clear, while the gene could be another candidate biomarker for PE risk prediction. This is an exploratory study and our findings were still required verification in further studies.