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

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40589522", "kind": "science-record", "revision": 4, "content_hash": "sha256:d1ab64c70e94120da1e234e09f46828e4ed8bd94c19f189d2c3a2af239f199fe", "created_at": "2026-11-01T20:30:40Z", "updated_at": "2026-11-01T22:56:43Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of Coagulation and Fibrinolysis-Associated Biomarkers With Implications for Preeclampsia.", "doi": "10.1155/genr/6637484", "pmid": "40589522", "authors": ["Yujie Liu", "Tingting Chen", "Cuifang Fan"], "journal": "Genetics research", "year": 2025, "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.", "claims": [{"id": "claim-method", "statement": "First, least absolute shrinkage and selection operator (LASSO) regression analysis and support vector machine-recursive feature elimination (SVM-RFE) were respectively implemented on intersected genes by glmnet package (version 4.1-4) [13] (setting family to binomial) and e1071 package (version 1.7-13) [14] to acquire the corresponding genes. After that, the genes gained from above two algorithms were intersected via online venn tool (https://jvenn.toulouse.inra.fr/app/example.html) to identify biomarkers. Finally, we constructed a diagnostic model based on the biomarkers utilizing the glm function. In order to evaluate the diagnostic performance of the diagnostic model for PE, we extracted the expression values of biomarkers in the GSE10588 training set, and plotted receiver operating characteristic (ROC) curve with the grouping information of samples in the dataset. We also validated the evaluation results by the same methods in GSE54618 validation set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40589522", "locator": "sections[5] \"2.3. Construction of a Diagnostic Model\" paragraph 2", "quote": "First, least absolute shrinkage and selection operator (LASSO) regression analysis and support vector machine-recursive feature elimination (SVM-RFE) were respectively implemented on intersected genes by glmnet package (version 4.1-4) [13] (setting family to binomial) and e1071 package (version 1.7-13) [14] to acquire the corresponding genes. After that, the genes gained from above two algorithms were intersected via online venn tool (https://jvenn.toulouse.inra.fr/app/example.html) to identify biomarkers. Finally, we constructed a diagnostic model based on the biomarkers utilizing the glm function. In order to evaluate the diagnostic performance of the diagnostic model for PE, we extracted the expression values of biomarkers in the GSE10588 training set, and plotted receiver operating characteristic (ROC) curve with the grouping information of samples in the dataset. We also validated the evaluation results by the same methods in GSE54618 validation set."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The provision of data will be in accordance with the specified requirements.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40589522", "locator": "sections[24] \"Data Availability Statement\" paragraph 2", "quote": "The provision of data will be in accordance with the specified requirements."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The diagnostic model comprising CYP19A1, C1QBP, GHR, and PSMA3 showed favorable diagnostic performance for preeclampsia, with AUC values exceeding 0.75 in both the training and validation datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40589522", "locator": "sections[15] \"3.3. Evaluation of the Diagnostic Model\" paragraph 2", "quote": "By intersecting the gene sets obtained from both machine learning approaches, four biomarkers were identified, namely, CYP19A1, C1QBP, GHR, and PSMA3 (Figure 3(c)). Notably, the diagnostic model demonstrated favorable performance for PE, as evidenced by the AUC values exceeding 0.75 in both the training and validation datasets (Figure 3(d))."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The diagnostic model comprising CYP19A1, C1QBP, GHR, and PSMA3 showed favorable diagnostic performance for preeclampsia, with AUC values exceeding 0.75 in both the training and validation datasets.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:40589522"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE10588"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE10588"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40589522", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1155/genr/6637484", "sha256": "sha256:298378b7eb10a544342a471393034d97c1220394abb9719820f2baf952092d06", "bytes": 28162, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1155/genr/6637484", "version": null, "note": "Full article fetched from PMC; 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After that, the genes gained from above two algorithms were intersected via online venn tool (https://jvenn.toulouse.inra.fr/app/example.html) to identify biomarkers. Finally, we constructed a diagnostic model based on the biomarkers utilizing the glm function. In order to evaluate the diagnostic performance of the diagnostic model for PE, we extracted the expression values of biomarkers in the GSE10588 training set, and plotted receiver operating characteristic (ROC) curve with the grouping information of samples in the dataset. We also validated the evaluation results by the same methods in GSE54618 validation set."], "order_basis": "analysis_only", "id": "assay:1"}]}