Unveiling immune tolerance pathways in preeclampsia placenta: implications for molecular targets and discovery of potential biomarkers.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38894741", "kind": "science-record", "revision": 2, "content_hash": "sha256:653d6d1ffdccbc1d329d24ae69f84bfcbe2111e04ffeb0c3800697d063b1f25c", "created_at": "2026-11-01T20:28:57Z", "updated_at": "2026-11-01T21:33:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Unveiling immune tolerance pathways in preeclampsia placenta: implications for molecular targets and discovery of potential biomarkers.", "doi": "10.3389/fendo.2024.1385154", "pmid": "38894741", "authors": ["Yantuanjin Ma", "Xingli Deng", "Ru Shen", "Hongqing Zhang", "Yuan Qian"], "journal": "Frontiers in endocrinology", "year": 2024, "abstract": "During pregnancy, there is a link between disruption of maternal immune tolerance and preeclampsia, but the molecular mechanisms that regulate maternal and fetal immune tolerance remain unclear. This study employs bioinformatics to identify new markers related to placental immune tolerance and explore their potential role in predicting preeclampsia. Analyzing preeclampsia-related gene expression profiles in the Gene Expression Omnibus (GEO) dataset reveals 211 differentially expressed genes (DEGs) in the placenta, mainly influencing immune cell differentiation and response pathways. Employing weighted gene co-expression network analysis (WGCNA) and lasso regression, four potential target genes (ANKRD37, CRH, LEP, SIGLEC6) are identified for potential prediction of preeclampsia. Validation using the GSE4707 dataset confirmed the diagnostic and predictive potential of these candidate genes. RT-qPCR verified up-regulation in the placenta, while ELISA showed their correlation with immune tolerance factors associated with placental immune tolerance. As a result of this study, identifies potential biomarkers associated with placental immunity and contributes to understanding the molecular mechanism of preeclampsia.", "claims": [{"id": "claim-method", "statement": "Utilizing 10-fold cross-validation to select the optimal λ, we identified 4 biomarkers (ANKRD37, CRH, LEP, and SIGLEC6) from the 26 intersecting genes as candidate genes through LASSO analysis ( Figures 4E, F ).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38894741", "locator": "sections[17] \"Screening biomarkers of preeclampsia using machine learning algorithm\" paragraph 2", "quote": "Utilizing 10-fold cross-validation to select the optimal λ, we identified 4 biomarkers (ANKRD37, CRH, LEP, and SIGLEC6) from the 26 intersecting genes as candidate genes through LASSO analysis ( Figures 4E, F )."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/ (GSE44711, GSE66273, GSE4707) and (Platforms GPL10558, GPL4133, and GPL1708).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38894741", "locator": "sections[23] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/ (GSE44711, GSE66273, GSE4707) and (Platforms GPL10558, GPL4133, and GPL1708)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Employing weighted gene co-expression network analysis (WGCNA) and lasso regression, four potential target genes (ANKRD37, CRH, LEP, SIGLEC6) are identified for potential prediction of preeclampsia.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38894741", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Employing weighted gene co-expression network analysis (WGCNA) and lasso regression, four potential target genes (ANKRD37, CRH, LEP, SIGLEC6) are identified for potential prediction of preeclampsia."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Employing weighted gene co-expression network analysis (WGCNA) and lasso regression, four potential target genes (ANKRD37, CRH, LEP, SIGLEC6) are identified for potential prediction of preeclampsia.", "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:38894741"], "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:GSE66273"], "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:GSE66273"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38894741", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2024.1385154", "sha256": "sha256:801b281e8aec1c7895d9c53f25cd3f81eda15fbdf8fa8487e464ce1fa862ac5b", "bytes": 46032, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fendo.2024.1385154", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11182985", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE66273", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE66273", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE66273", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/ (GSE44711, GSE66273, GSE4707) and (Platforms GPL10558, GPL4133, and GPL1708).", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/ (GSE44711, GSE66273, GSE4707) and (Platforms GPL10558, GPL4133, and GPL1708).", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1618441", "GSM1618442", "GSM1618443", "GSM1618444", "GSM1618445", "GSM1618446", "GSM1618447", "GSM1618448", "GSM1618449", "GSM1618450", "GSM1618451", "GSM1618452", "GSM1618453", "GSM1618454", "GSM1618455", "GSM1618456", "GSM1618457"], "sample_count": 17, "group_sizes": null, "group_sizes_basis": null, "platform": "4133", "taxon": "Homo sapiens", "title": "Genome-wide analysis of placental gene expression in severe preterm preeclampsia", "resolution_checked_at": "2026-11-01T18:46:34Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE66273 not downloaded", "asset:data:geo:GSE66273 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": null, "specific": null, "label": "LASSO analysis with 10-fold cross-validation", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Utilizing 10-fold cross-validation to select the optimal λ, we identified 4 biomarkers (ANKRD37, CRH, LEP, and SIGLEC6) from the 26 intersecting genes as candidate genes through LASSO analysis ( Figures 4E, F )."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "method", "blocked_on": "method:not_determinable", "axes": {"method": {"tier": "R4", "expressible": false, "required_operations": [], "missing_operations": [], "basis": "The reader quoted the method the paper names (LASSO analysis with 10-fold cross-validation), but the corpus's method extractor could not map it onto a DSL operation, so the method is published but not expressible."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R1", "reachable": true, "accessions": ["(GSE44711", "GSE66273", "GSE4707) and (Platforms GPL10558"], "resolved_accessions": ["GSE66273"], "statements": ["The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/ (GSE44711, GSE66273, GSE4707) and (Platforms GPL10558, GPL4133, and GPL1708)."], "basis": "The paper names GSE66273 in Gene Expression Omnibus (GEO) database and the accession resolves in the repository. No dataset was downloaded and no licence was read."}}, "can_run": {"dsl_operations": [], "assets": ["source:pmid:38894741", "data:geo:GSE66273"], "steps": ["run_analysis"]}, "must_build": {"dsl_operations": [], "converters": [], "readers": [], "capabilities": [], "data_gaps": ["data:geo:GSE66273"]}, "gaps": ["method:not_determinable"]}, "review": {"state": "partial", "machine_critique": {"kind": "extraction_review", "reader": {"model": "MiniMax-M3.1-Flash", "provider": "minimax", "provider_family": "minimax", "read_at": "2026-11-01T19:21:38Z", "independence": "single_reader"}, "evidence_check": {"method": "verbatim", "availability": "verbatim"}, "calls": 5, "usage": {"prompt_tokens": 12746, "completion_tokens": 2150, "cached_tokens": 128}}, "human_critique": null, "stages": [{"id": "extraction", "state": "complete", "reader": "MiniMax-M3.1-Flash", "provider": "minimax"}], "gaps": ["review.independent_reader", "review.machine_critique", "review.human_critique"]}, "execution": {"state": "absent", "status": "not_run", "mode": null, "runs": [], "gaps": ["execution.not_run"]}, "provenance": {"entities": [{"id": "pmid:38894741", "type": "paper", "label": "pmid:38894741"}, {"id": "source:pmid:38894741", "type": "manuscript", "label": "PMID 38894741 full text"}, {"id": "data:geo:GSE66273", "type": "dataset", "label": "GEO GSE66273"}], "activities": [{"id": "activity-reading-sci-2026-11-01", "type": "classification", "used": [{"id": "pmid:38894741", "type": "paper", "label": "pmid:38894741"}, {"id": "source:pmid:38894741", "type": "manuscript", "label": "PMID 38894741 full text"}, {"id": "data:geo:GSE66273", "type": "dataset", "label": "GEO GSE66273"}], "agent": "agent:corpus-assay-engine"}, {"id": "activity-read-38894741", "type": "extraction", "used": ["source:pmid:38894741"], "agent": "agent:minimax-reader"}, {"id": "activity-resolve-GSE66273", "type": "accession_resolution", "used": ["data:geo:GSE66273"], "agent": "agent:corpus-assay-engine"}], "agents": [{"id": "agent:corpus-assay-engine", "type": "software", "label": "Corpus assay engine"}, {"id": "agent:minimax-reader", "type": "software", "label": "MiniMax literature reader"}], "source_run_id": "reading-sci-2026-11-01", "dsl_catalogue_fingerprint": "343fb30756ba6ec3", "capability_table_fingerprint": "2cb3e42f047d2ecf", "snapshot_id": "ba34b49490d9bfaf"}, "lineage": {"parent_id": "pmid:38894741", "parent_hash": "sha256:ddb3171635483286e423a40d2924f7f00bb070315c3cd62cf107afbf93ff29bd", "fork_of": null, "generation": 1}, "gaps": ["asset:data:geo:GSE66273 licence not read", "asset:data:geo:GSE66273 not downloaded", "execution.not_run", "method.reader_named_unmapped", "review.human_critique", "review.independent_reader", "review.machine_critique"], "assays": [{"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["edictive potential of these candidate genes. RT-qPCR verified up-regulation in the placenta, whil", "ctive potential of these candidate genes. RT-qPCR verified up-regulation in the placenta, whil"], "order_basis": "paper_text_order:993", "id": "assay:1"}, {"role": "data_generation", "definition_id": "elisa", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["erified up-regulation in the placenta, while ELISA showed their correlation with immune toleran"], "order_basis": "paper_text_order:1047", "id": "assay:2"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.83, "evidence": ["d for gene set enrichment analysis (GSEA) of transcriptome changes. The aim was to elucidate the immuno", "ased on the ImmuneSigDB database to evaluate transcriptome changes. A number of immune-related pathways"], "order_basis": "paper_text_order:1712", "id": "assay:3"}]}