{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T22:28:31Z","timestamp":1773786511321,"version":"3.50.1"},"reference-count":46,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T00:00:00Z","timestamp":1750636800000},"content-version":"vor","delay-in-days":173,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genetics Research"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    <jats:bold>Background:<\/jats:bold>\n                    Coagulation system abnormalities contribute to clinical manifestations in preeclampsia (PE), but the mechanisms of coagulation and fibrinolysis in PE are unclear.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Methods:<\/jats:bold>\n                    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\u2010related 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\u2010RFE 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\u2010miRNA\u2010mRNA network was constructed.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Results:<\/jats:bold>\n                    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\u2009T 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\u2010one potential therapeutic agents were predicted, as well as n circRNA\u2010miRNA\u2010mRNA network composed of 73 nodes and 88 edges.\n                  <\/jats:p>\n                  <jats:p>\n                    <jats:bold>Conclusion:<\/jats:bold>\n                    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.\n                  <\/jats:p>","DOI":"10.1155\/genr\/6637484","type":"journal-article","created":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T09:18:38Z","timestamp":1750670318000},"source":"Crossref","is-referenced-by-count":0,"title":["Identification of Coagulation and Fibrinolysis\u2010Associated Biomarkers With Implications for 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