Bioinformatic analysis reveals lysosome-related biomarkers and molecular subtypes in preeclampsia: novel insights into the pathogenesis of preeclampsia.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37576559", "kind": "science-record", "revision": 5, "content_hash": "sha256:1cf77e3e1fbfa8c6573a135ebd4c92f8669ceb01775d9b3b82ed8053e42fb2d2", "created_at": "2026-11-01T19:02:22Z", "updated_at": "2026-11-01T22:52:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatic analysis reveals lysosome-related biomarkers and molecular subtypes in preeclampsia: novel insights into the pathogenesis of preeclampsia.", "doi": "10.3389/fgene.2023.1228110", "pmid": "37576559", "authors": ["Yao Chen", "Miao Liu", "Yonghong Wang"], "journal": "Frontiers in genetics", "year": 2023, "abstract": "Background: The process of lysosomal biogenesis and exocytosis in preeclamptic placentae plays a role in causing maternal endothelial dysfunction. However, the specific lysosome-associated markers relevant to preeclampsia (PE) are not well-defined. Our objective is to discover new biomarkers and molecular subtypes associated with lysosomes that could improve the diagnosis and treatment of PE. Methods: We obtained four microarray datasets related to PE from the Gene Expression Omnibus (GEO) database. The limma package was utilized to identify genes that were differentially expressed between individuals with the disease and healthy controls. The logistic regression analysis was used to identify core diagnostic biomarkers, which were subsequently validated by independent datasets and clinical samples. Additionally, a consensus clustering method was utilized to distinguish between different subtypes of PE. Following this, functional enrichment analysis, GSEA, GSVA, and immune cell infiltration were conducted to compare the two subtypes and identify any differences in their functional characteristics and immune cell composition. Results: We identified 16 PE-specific lysosome-related genes. Through regression analysis, two genes, GNPTG and CTSC, were identified and subsequently validated in the external validation cohort GSE60438 and through qRT-PCR experiment. A nomogram model for the diagnosis of PE was developed and evaluated using these two genes. The model had a remarkably high predictive power (AUC values of the training set, validation set, and clinical samples were 0.897, 0.788, and 0.979, respectively). Additionally, two different molecular subtypes (C1 and C2) were identified, and we found notable variations in the levels of immune cells present in the two subtypes. Conclusion: Our results not only offered a classification system but also identified novel diagnostic biomarkers for PE patients. Our findings offered an additional understanding of how to categorize PE patients and also highlighted potential avenues for creating treatments for individuals with PE.", "claims": [{"id": "claim-method", "statement": "We utilized the “limma” package in R to screen for differentially expressed LRs between HC and PE samples, applying a threshold of p.adjust <0.05 and |log fold change (FC)| ≥ 0.5.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37576559", "locator": "sections[4] \"Identification of differentially expressed LRs\" paragraph 2", "quote": "We utilized the “limma” package in R to screen for differentially expressed LRs between HC and PE samples, applying a threshold of p.adjust <0.05 and |log fold change (FC)| ≥ 0.5."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37576559", "locator": "sections[22] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The limma analysis of placental microarray data from PE and healthy-control groups identified 16 differentially expressed lysosome-related genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37576559", "locator": "sections[4] \"Identification of differentially expressed LRs\" paragraph 2", "quote": "We utilized the “limma” package in R to screen for differentially expressed LRs between HC and PE samples, applying a threshold of p.adjust <0.05 and |log fold change (FC)| ≥ 0.5."}, {"asset": "source:pmid:37576559", "locator": "sections[12] \"Identification of differentially expressed LRs in PE\" paragraph 2", "quote": "The genes obtained from the DEGs and LRs were intersected, and 16 differentially expressed LRs were obtained (Figure 2B)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The limma analysis of placental microarray data from PE and healthy-control groups identified 16 differentially expressed lysosome-related genes.", "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:37576559"], "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. 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