Key result
Three-gene signature accurately distinguishes hypertension in pregnancy from healthy controls.
Why the study?
Hypertensive disorders of pregnancy affect 5-10% of pregnancies, and potential diagnostic signatures remain to be identified.
Can a three-gene diagnostic signature (LEP, PRL, IGFBP1) accurately identify hypertension in pregnancy?
Observational
Can a three-gene diagnostic signature (LEP, PRL, IGFBP1) accurately identify hypertension in pregnancy?
A three-gene signature (LEP, PRL, IGFBP1) may serve as a potential diagnostic biomarker for hypertension in pregnancy.
Answers are grounded in this paper and cite their sources.
May identify candidate signatures for hypertensive pregnancy disorders; hypothesis-generating and requires prospective validation before any clinical use.
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.
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Liu et al. (2021) conducted an observational in Hypertension in pregnancy. Three-gene diagnostic signature (LEP, PRL, IGFBP1) vs. Healthy subjects was evaluated on Separation of patients with hypertension in pregnancy from healthy subjects. A predictive logistic regression model based on three genes (LEP, PRL, and IGFBP1) efficiently separated patients with hypertension in pregnancy from healthy subjects.
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