Identification of critical biomarkers and immune infiltration in preeclampsia through bioinformatics and machine learning methods.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39934690", "kind": "science-record", "revision": 3, "content_hash": "sha256:ad2ea67dd614370713843f1cb408db04ad4db2b12b3b572a4b2d295086f6cd7c", "created_at": "2026-11-01T20:30:12Z", "updated_at": "2026-11-01T22:13:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of critical biomarkers and immune infiltration in preeclampsia through bioinformatics and machine learning methods.", "doi": "10.1186/s12884-025-07257-0", "pmid": "39934690", "authors": ["Weiwen Li", "Lijun Zhong", "Kewen Zhao", "Jincheng Xie", "Shaodong Deng", "Yunyong Fang"], "journal": "BMC pregnancy and childbirth", "year": 2025, "abstract": "BACKGROUND: Preeclampsia (PE) is a multisystem progressive disease that occurs during pregnancy. Previous studies have shown that the immune system is involved in the placental trophoblast function and the pathological process of uterine vascular remodeling in PE. However, its molecular mechanism is still unclear. This study aimed to identify critical genes and immune cells involved in the pathological process of PE.\nMETHODS: The PE-related GSE74341 and GSE160888 datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differential expression analysis, weighted gene co-expression network analysis (WGCNA), and least absolute shrinkage and selection operator (LASSO) analysis were combined to screen the PE-related DEGs. Receiver operating characteristic (ROC) analysis was used to evaluate the diagnostic specificity of obtained DEGs, and the GSE35574 dataset was used for preliminary validation. Furthermore, the single-sample Gene Set Enrichment Analysis (ssGSEA) was used to elucidate the correlation between the DEGs and the 28 types of infiltrating immune cells in PE. Real-time reverse transcription polymerase chain reaction (RT-PCR) was used to verify the differential expression of DEGs in the PE placental tissues.\nRESULTS: A total of 143 DEGs (DE-mRNAs) were screened using the PE datasets. The analysis of DEG modules and LASSO logistic regression were used to identify high-temperature requirement factor A4 (HtrA4), tumour suppressor candidate 3 (TUSC3), endothelial protein C receptor gene (PROCR), claudin 3 (CLDN3), and thioredoxin binding protein (TXNIP) as the hub DEGs in PE. Furthermore, validation with the GSE35574 dataset and ROC analysis was used to clarify that the HTRA4, PROCR, and TXNIP genes are potential markers of PE and are closely related to the infiltrating immune cells in PE, such as gamma delta T cells, mast cells, natural killer cells, and T follicular helper cells. Finally, differential HTRA4, PROCR, and TXNIP expression were confirmed in PE placental tissues (p < 0.001).\nCONCLUSION: HTRA4, PROCR, and TXNIP can be used as potential PE biomarkers to provide a new strategy for early diagnosing and treating PE.", "claims": [{"id": "claim-method", "statement": "Differences between groups were analyzed with a t-test using SPSS 19.0, and graphs were plotted using GraphPad 6.0 software.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39934690", "locator": "sections[12] \"Statistical analysis\" paragraph 2", "quote": "Differences between groups were analyzed with a t-test using SPSS 19.0, and graphs were plotted using GraphPad 6.0 software."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analyzed during the current study are available in the Gene Expression Omnibus (GEO) database. 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