# Identification of Coagulation and Fibrinolysis-Associated Biomarkers With Implications for Preeclampsia

## TL;DR

This study identifies four biomarkers linked to coagulation and fibrinolysis in preeclampsia, offering insights into diagnosis and treatment.

## Contribution

The novel contribution is the identification of a diagnostic model with four coagulation/fibrinolysis-related biomarkers for preeclampsia.

## Key findings

- A diagnostic model with CYP19A1, C1QBP, GHR, and PSMA3 was identified for preeclampsia.
- Immune cell infiltration analysis revealed significant differences in T helper and regulatory T cells.
- A circRNA-miRNA-mRNA network with 73 nodes and 88 edges was constructed.

## Abstract

Background: Coagulation system abnormalities contribute to clinical manifestations in preeclampsia (PE), but the mechanisms of coagulation and fibrinolysis in PE are unclear.

Methods: We utilized the Gene Expression Omnibus (GEO) database to obtain the GSE10588 training set and GSE54618 validation set. From GeneCards, we extracted 514 coagulation and fibrinolysis-related genes (CFRGs). Differential expression analysis identified 1521 DEGs in the GSE10588 training set. WGCNA revealed the salmon module (778 genes) as the key module. LASSO and SVM-RFE methods identified four biomarkers (CYP19A1, C1QBP, GHR, and PSMA3) for a diagnostic model. GSEA was performed on the biomarkers. Immune cell infiltration and therapeutic agents for the biomarkers were analyzed. A circRNA-miRNA-mRNA network was constructed.

Results: The salmon module showed the highest correlation with PE and normal samples. The diagnostic model comprised CYP19A1, C1QBP, GHR, and PSMA3. Immune cell analysis revealed significant differences, including type 2 T helper cells and regulatory T cells. C1QBP correlated positively with effector memory CD4 T cells, while PSMA3 had a negative correlation with CD56dim natural killer cells. Sixty-one potential therapeutic agents were predicted, as well as n circRNA-miRNA-mRNA network composed of 73 nodes and 88 edges.

Conclusion: Our bioinformatic analysis resulted in a diagnostic model (CYP19A1, C1QBP, GHR, and PSMA3) for PE related to coagulation and fibrinolysis. We also conducted immune microenvironment and drug sensitivity analyses, providing insights into PE diagnosis and treatment.