A Potential Three-Gene-Based Diagnostic Signature for Hypertension in Pregnancy.

Abstract

BACKGROUND:

Hypertensive disorders of pregnancy affect approximately 5-10% of all pregnancies, and this study aims to identify potential diagnostic signatures.

METHODS:

We downloaded the mRNA profiles of GSE75010 (placenta samples) and GSE48424 (blood samples) datasets with or without hypertension in pregnancy from the Gene Expression Omnibus database. Differential expression analysis was performed on the placenta samples using limma package of R language. GO terms and KEGG pathways enrichment analyses were performed on the placenta samples by the clusterProfiler package of R language. Infiltrating immune cell proportion of the placenta samples was evaluated using CIBERSORT software. The key genes involved in hypertension in pregnancy were screened from protein-protein interaction (PPI) network constructed based on the differentially expressed genes (DEGs). The logistic regression model was constructed by the glm package of R language, and receiver operating characteristic (ROC) curve was plotted to determine the accuracy of the model.

RESULTS:

For the placenta samples, a total of 104 DEGs were identified, and 39 GO terms and 7 KEGG pathways were significantly enriched based on these 104 genes. Furthermore, the analysis of infiltrating immune cells indicated that the difference in the amount of immune cells might be the potential cause of hypertension in pregnancy. The logistic regression model was constructed based on three optimal genes (LEP, PRL and IGFBP1) screened from PPI network and could efficiently separate patients with hypertension in pregnancy from healthy subjects.

CONCLUSION:

A predictive model based on three potential genes LEP, PRL and IGFBP1 was obtained, suggesting that these genes might be potential diagnostic signatures for hypertension in pregnancy.

• Authors: Yan Liu, Zhenglu Wang, Lin Zhao
• Journal: International journal of general medicine (Int J Gen Med) Vol. 14 Pg. 6847-6856 ( 2021) ISSN: 1178-7074 [Print] New Zealand
• PMID: 34703289 (Publication Type: Journal Article)
• Copyright: © 2021 Liu et al.