Shared Biomarkers and Potential Mechanisms Between Hashimoto's Thyroiditis and Recurrent Miscarriage Revealed by Transcriptomics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41216601", "kind": "science-record", "revision": 4, "content_hash": "sha256:0403598f28f3e5fe918053af0e99b29bfe37011789b52cfc4282353c07b043dc", "created_at": "2026-11-01T20:31:15Z", "updated_at": "2026-11-01T22:57:26Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Shared Biomarkers and Potential Mechanisms Between Hashimoto's Thyroiditis and Recurrent Miscarriage Revealed by Transcriptomics Analysis.", "doi": "10.2147/IJGM.S552065", "pmid": "41216601", "authors": ["Zengmei Cheng", "Shuyun Zhao", "Lu Yang", "Yaqiong Xu", "Peiyu Zhang", "Sha Chen", "Hua Zhou"], "journal": "International journal of general medicine", "year": 2025, "abstract": "Background: More and more Research has shown that Hashimoto's thyroiditis (HT) is significantly associated with recurrent miscarriage (RM), but the specific mechanism is not yet clear. This study aimed to identify the key shared biomarkers between HT and RM using bioinformatics methods, reveal the potential molecular mechanisms they were involved in and the characteristics of the immune microenvironment, and provide new theoretical basis and potential diagnostic and therapeutic targets for the association between these two diseases.\nMethods: This study adopted an integrated transcriptomic analysis strategy. First, the HT thyroid tissue dataset (GSE138198) and RM endometrial tissue datasets (GSE165004 and GSE26787) were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened, and the intersection was taken to identify key genes. Further verification was conducted through the protein-protein interaction (PPI) network to screen candidate biomarkers. Subsequently, the final biomarkers were determined through consistency verification of expression levels. Gene Set Enrichment Analysis (GSEA) was used to reveal biomarker-related pathways, and the ssGSEA algorithm was applied to quantify immune cell infiltration for analyzing its association with the immune microenvironment. Finally, targeted drugs were predicted via molecular docking, and experimental verification was performed using an HT animal model.\nResults: CFL1 and TRAPPC1 were identified as biomarkers, and their expression levels were up-regulated in disease groups. A nomogram with superior diagnostic performance was constructed to predict the occurrence of RM. In the GSE138198 dataset, biomarkers CFL1 and TRAPPC1 were found to be enriched in multiple pathways, like \"graft-versus-host disease\", \"autoimmune thyroid disease\", and \"antigen processing and presentation\". In the GSE165004 dataset, biomarkers were enriched in multiple pathways, like \"ribosome\", \"Huntington's disease\", and \"cell adhesion molecules (CAMs)\". Additionally, the abundance of infiltration of monocytes and eosinophils infiltration showed significant differences between HT and RM patients (p < 0.05). Biomarkers showed significant positive correlations with monocytes and eosinophils in HT and RM, respectively. Moreover, artenimol and S-palmitoyl-L-cysteine might be potential therapeutic drugs for HT and RM.\nConclusion: CFL1 and TRAPPC1 were found to be common biomarkers for HT and RM in this study. These genes were thoroughly investigated and analyzed, yielding novel insights for both fundamental experimental research and early clinical diagnosis and treatment of disease.", "claims": [{"id": "claim-method", "statement": "In the GSE138198 dataset, to identify differentially expressed genes in HT (HT-DEGs), the limma package (v 3.54.0)21 was used to perform differential expression analysis between HT and control groups (|log2Fold Change (FC)| > 0.5, adjusted p-value (adj.p) < 0.05).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41216601", "locator": "sections[4] \"Differential Expression Analysis\" paragraph 2", "quote": "In the GSE138198 dataset, to identify differentially expressed genes in HT (HT-DEGs), the limma package (v 3.54.0)21 was used to perform differential expression analysis between HT and control groups (|log2Fold Change (FC)| > 0.5, adjusted p-value (adj.p) < 0.05)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed in this study (GSE138198, GSE165004, GSE26787) were publicly available in the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/gds).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41216601", "locator": "sections[39] \"Data Sharing Statement\" paragraph 2", "quote": "The datasets analyzed in this study (GSE138198, GSE165004, GSE26787) were publicly available in the Gene Expression Omnibus (GEO) repository (https://www.ncbi.nlm.nih.gov/gds)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "CFL1 and TRAPPC1 were identified as shared biomarkers for HT and RM, and a nomogram based on them achieved an AUC of 0.927 for RM diagnosis.", "kind": "quantitative", "target": {"value": 0.927, "unit": "auc", "tolerance": null, "source": "The ROC curve emphasized the excellent diagnostic value of the nomogram, with an AUC of 0.927 (Figure 3c)."}, "evidence": [{"asset": "source:pmid:41216601", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "CFL1 and TRAPPC1 were identified as biomarkers, and their expression levels were up-regulated in disease groups."}, {"asset": "source:pmid:41216601", "locator": "sections[27] \"Nomogram with Excellent Performance for Diagnosis of HT\" paragraph 2", "quote": "The ROC curve emphasized the excellent diagnostic value of the nomogram, with an AUC of 0.927 (Figure 3c)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "CFL1 and TRAPPC1 were identified as shared biomarkers for HT and RM, and a nomogram based on them achieved an AUC of 0.927 for RM diagnosis.", "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:41216601"], "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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