Identification of oxidative stress-related biomarkers in uterine leiomyoma: a transcriptome-combined Mendelian randomization analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39640883", "kind": "science-record", "revision": 3, "content_hash": "sha256:185f3d230833b3149134b1ebde5c31edc16fe3bf8422c618a9a18c14cb035a36", "created_at": "2026-11-01T20:29:55Z", "updated_at": "2026-11-01T22:13:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of oxidative stress-related biomarkers in uterine leiomyoma: a transcriptome-combined Mendelian randomization analysis.", "doi": "10.3389/fendo.2024.1373011", "pmid": "39640883", "authors": ["Yingxiao Li", "Haoyue Chen", "Hao Zhang", "Zhaochen Lin", "Liang Song", "Chuanliang Zhao"], "journal": "Frontiers in endocrinology", "year": 2024, "abstract": "Background: Oxidative stress has been implicated in the pathogenesis of uterine leiomyoma (ULM) with an increasing incidence. This study aimed to identify potential oxidative stress-related biomarkers in ULM using transcriptome data integrated with Mendelian randomization (MR) analysis.\nMethods: Data from GSE64763 and GSE31699 in the Gene Expression Omnibus (GEO) were included in the analysis. Oxidative stress-related genes (OSRGs) were identified, and the intersection of differentially expressed genes (DEGs), Weighted Gene Co-expression Network Analysis (WGCNA) genes, and OSRGs was used to derive differentially expressed oxidative stress-related genes (DE-OSRGs). Biomarkers were subsequently identified via MR analysis, followed by Gene Set Enrichment Analysis (GSEA) and immune infiltration analysis. Nomograms, regulatory networks, and gene-drug interaction networks were constructed based on the identified biomarkers.\nResults: A total of 883 DEGs were identified between ULM and control samples, from which 42 DE-OSRGs were screened. MR analysis revealed four biomarkers: ANXA1, CD36, MICB, and PRDX6. Predictive nomograms were generated based on these biomarkers. ANXA1, CD36, and MICB were significantly enriched in chemokine signaling and other pathways. Notably, ANXA1 showed strong associations with follicular helper T cells, resting mast cells, and M0 macrophages. CD36 was positively correlated with resting mast cells, while MICB was negatively correlated with macrophages. Additionally, ANXA1 displayed strong binding energy with amcinonide, and MICB with ribavirin.\nConclusion: This study identified oxidative stress-related biomarkers (ANXA1, CD36, MICB, and PRDX6) in ULM through transcriptomic and MR analysis, providing valuable insights for ULM therapeutic research.", "claims": [{"id": "claim-method", "statement": "MR analysis was conducted to investigate the causal relationship between the exposure factors (DE-OSRGs) and the outcome (ULM).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39640883", "locator": "sections[6] \"Mendelian randomization analysis\" paragraph 2", "quote": "MR analysis was conducted to investigate the causal relationship between the exposure factors (DE-OSRGs) and the outcome (ULM)."}], "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/s.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39640883", "locator": "sections[31] \"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/s."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Based on the IVW method, four biomarkers—ANXA1 (p = 0.0474, odds ratio (OR) = 1.021), CD36 (p = 0.0013, OR = 1.074), MICB (p = 0.0025, OR = 1.032), and PRDX6 (p = 0.0215, OR = 1.042)—demonstrated significant causal relationships with ULM, all with p < 0.05.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39640883", "locator": "sections[21] \"Identification of four biomarkers through MR analysis\" paragraph 2", "quote": "Based on the IVW method, four biomarkers—ANXA1 (p = 0.0474, odds ratio (OR) = 1.021), CD36 (p = 0.0013, OR = 1.074), MICB (p = 0.0025, OR = 1.032), and PRDX6 (p = 0.0215, OR = 1.042)—demonstrated significant causal relationships with ULM, all with p < 0.05."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Based on the IVW method, four biomarkers—ANXA1 (p = 0.0474, odds ratio (OR) = 1.021), CD36 (p = 0.0013, OR = 1.074), MICB (p = 0.0025, OR = 1.032), and PRDX6 (p = 0.0215, OR = 1.042)—demonstrated significant causal relationships with ULM, all with p < 0.05.", "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:39640883"], "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:GSE31699"], "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:GSE31699"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39640883", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fendo.2024.1373011", "sha256": "sha256:a191d1786316fc86bbe765b75a58b66cbd479956fa7444ef2d805ec6da1bb460", "bytes": 66064, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fendo.2024.1373011", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11617171", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE31699", "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=GSE31699", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE31699", "version": null, "note": "The original contributions presented in the study are included in the article/ Supplementary Material . 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