Identification of immune-related genes and molecular subtypes associated with preeclampsia via bioinformatics analysis and experimental validation.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40442789", "kind": "science-record", "revision": 3, "content_hash": "sha256:ca642be1ed6256eb874d807225882b2bf453d5f991688046221afc89d18261af", "created_at": "2026-11-01T20:30:34Z", "updated_at": "2026-11-01T22:14:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune-related genes and molecular subtypes associated with preeclampsia via bioinformatics analysis and experimental validation.", "doi": "10.1186/s41065-025-00458-9", "pmid": "40442789", "authors": ["Tingting Zhao", "Ying Peng"], "journal": "Hereditas", "year": 2025, "abstract": "BACKGROUND: Preeclampsia (PE) is a pregnancy disorder that occurs after 20 weeks of pregnancy. The objective of this study was to identify potential immune-related biomarkers and molecular subtypes for the treatment of PE.\nMETHODS: Three datasets of GSE10588, GSE25906 and GSE48424 were downloaded from the Gene Expression Omnibus (GEO) database. The names of immune-related genes were retrieved from the ImmPort immune database. To screen the differentially expressed immune-related genes, the \"limma\" R package was used. An analysis of logistic regression was used to identify the key genes and a nomogram was constructed using these key genes. These key gene expression profiles were further validated using qRT-PCR. In addition, the landscape of immune cell infiltration was investigated using the CIBERSORTX software. The potential molecular subtypes of PE were also investigated using the \"ConsensusClusterPlus\" R package.\nRESULTS: The 103 immune-related genes differentially expressed were identified, including 47 up-regulated genes and 56 down-regulated genes. Univariate and multivariate logistic regression analysis was used to screen five key genes, including CCL24, ENG, LCP2, GNAI1 and FLT3. The key genes were strongly associated with immune cell infiltration. Two molecular subtypes (C1 and C2) were identified. Both exhibited distinct levels of immune cell infiltration and gene expression.\nCONCLUSION: This study identified five key genes, as well as immune-related subtypes, that could provide potential therapeutic targets and aid in the design of more precise PE immunotherapy.", "claims": [{"id": "claim-method", "statement": "An analysis of logistic regression was used to identify the key genes and a nomogram was constructed using these key genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40442789", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "An analysis of logistic regression was used to identify the key genes and a nomogram was constructed using these key genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used in this study are available in the GEO database repository, (http://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40442789", "locator": "sections[32] \"notes\" paragraph 1", "quote": "The datasets used in this study are available in the GEO database repository, (http://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Univariate and multivariate logistic regression analysis was used to screen five key genes, including CCL24, ENG, LCP2, GNAI1 and FLT3.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40442789", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Univariate and multivariate logistic regression analysis was used to screen five key genes, including CCL24, ENG, LCP2, GNAI1 and FLT3."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Univariate and multivariate logistic regression analysis was used to screen five key genes, including CCL24, ENG, LCP2, GNAI1 and FLT3.", "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:40442789"], "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:40442789", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s41065-025-00458-9", "sha256": 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