Integrative transcriptomic analysis identifies shared EndMT-related gene signatures in endometriosis and recurrent miscarriage.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41120378", "kind": "science-record", "revision": 4, "content_hash": "sha256:f214f3e0a4d0eac91c1dc23f7fe03b17c79645cb0e3a05a5f5bc8ebca17c5aa2", "created_at": "2026-11-01T20:31:10Z", "updated_at": "2026-11-01T22:57:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative transcriptomic analysis identifies shared EndMT-related gene signatures in endometriosis and recurrent miscarriage.", "doi": "10.1038/s41598-025-20302-4", "pmid": "41120378", "authors": ["Yue Liang", "Liangcheng Yu", "Qingde Zhou", "Danjie Su", "Lu Wang", "Cong Li", "Jingjing Wang", "Haihui Wang", "Jie Dong", "Xifeng Xiao", "Xiaohong Wang"], "journal": "Scientific reports", "year": 2025, "abstract": "Endometriosis (EMs) and recurrent miscarriage (RM) represent major reproductive health challenges. This study investigates the involvement of endothelial-mesenchymal transition (EndMT) in these conditions through integrative bioinformatics analysis, focusing on the dysregulation of EndMT-related genes (EndMTRGs). Transcriptomic datasets of EMs and RM were retrieved from the gene expression omnibus (GEO) database. Specifically, GSE120103 includes 18 endometriosis and 18 control samples, and GSE165004 includes 24 recurrent miscarriage and 24 control samples. Differentially expressed gene (DEG) analysis and EndMTRG profiling were performed to identify key pathways and hub genes associated with EndMT. Functional enrichment analyses, including gene ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and gene set enrichment analysis (GSEA), were conducted. A protein-protein interaction (PPI) network was constructed via the STRING database, and hub genes were identified using cytoHubba algorithms. Immune cell infiltration patterns were evaluated through single-sample GSEA (ssGSEA). Additionally, support vector machine recursive feature elimination (SVM-RFE) was applied as a supplementary method to assist key gene selection, and nomograms were developed for preliminary risk prediction modeling. A total of 13 EndMTRGs were identified, with key genes such as FGF2, ITGB1, VIM, NR4A1, MAPK1, SMAD1, TUBB3, and CDH11 validated across external datasets. ROC curve analysis demonstrated high diagnostic performance of these genes. Regulatory networks involving RNA-binding proteins and transcription factors were delineated. Immune cell-related gene set enrichment analysis (ssGSEA) revealed distinct immune signatures, notably involving gamma-delta T (γδ T) cells and monocytes in EMs, and T follicular helper (Tfh) cells and natural killer (NK) cells in RM. Nomograms based on key genes exhibited reasonable predictive performance. This study elucidates the dysregulated EndMT landscape shared by endometriosis and recurrent miscarriage, identifying common gene signatures and immune features. These findings provide molecular insights into the shared pathogenesis of these conditions and highlight potential targets for future translational research.", "claims": [{"id": "claim-method", "statement": "Differential gene expression analysis was performed using the Limma R package (version 3.58.1) on both the Endometriosis/Control and RM/Control groups24,25.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41120378", "locator": "sections[4] \"Identification of differentially expressed genes (DEGs)\" paragraph 2", "quote": "Differential gene expression analysis was performed using the Limma R package (version 3.58.1) on both the Endometriosis/Control and RM/Control groups24,25."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The transcriptomic datasets analyzed during this study are publicly available in the Gene Expression Omnibus (GEO) repository: GSE120103 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120103) GSE165004 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165004) Selected source code and processed data files used in this study are provided as supplementary material (Supplementary_Code_and_Data.zip) for transparency and reproducibility. This includes scripts for data cleaning, differential gene expression, enrichment analyses (GO/KEGG/GSEA), PPI construction and immune infiltration profiling. Additional processed data and scripts are available upon reasonable request from the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41120378", "locator": "sections[27] \"Data availability\" paragraph 2", "quote": "The transcriptomic datasets analyzed during this study are publicly available in the Gene Expression Omnibus (GEO) repository: GSE120103 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120103) GSE165004 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE165004) Selected source code and processed data files used in this study are provided as supplementary material (Supplementary_Code_and_Data.zip) for transparency and reproducibility. This includes scripts for data cleaning, differential gene expression, enrichment analyses (GO/KEGG/GSEA), PPI construction and immune infiltration profiling. Additional processed data and scripts are available upon reasonable request from the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC curves (Fig. 9 B–C, E–F) showed high discriminative ability, with the AUC for the nomogram predicting endometriosis being 0.925 (95% CI: 0.89–1.00) and for recurrent miscarriage being 0.951 (95% CI: 0.85–0.99).", "kind": "quantitative", "target": {"value": 95.0, "unit": "per cent", "tolerance": null, "source": "ROC curves (Fig. 9 B–C, E–F) showed high discriminative ability, with the AUC for the nomogram predicting endometriosis being 0.925 (95% CI: 0.89–1.00) and for recurrent miscarriage being 0.951 (95% CI: 0.85–0.99)."}, "evidence": [{"asset": "source:pmid:41120378", "locator": "sections[22] \"Identification of hub genes and construction and evaluation of predictive models\" paragraph 3", "quote": "ROC curves (Fig. 9 B–C, E–F) showed high discriminative ability, with the AUC for the nomogram predicting endometriosis being 0.925 (95% CI: 0.89–1.00) and for recurrent miscarriage being 0.951 (95% CI: 0.85–0.99)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "ROC curves (Fig. 9 B–C, E–F) showed high discriminative ability, with the AUC for the nomogram predicting endometriosis being 0.925 (95% CI: 0.89–1.00) and for recurrent miscarriage being 0.951 (95% CI: 0.85–0.99).", "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:41120378"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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