Bioinformatics analysis of ferroptosis-related biomarkers and potential drug predictions in doxorubicin-induced cardiotoxicity.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40342975", "kind": "science-record", "revision": 4, "content_hash": "sha256:fd537c27bc65b81e39ef057e29f93bf4fe9e880eb0d2d5af04dbf8e72e6d1da3", "created_at": "2026-11-01T20:30:30Z", "updated_at": "2026-11-01T22:56:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analysis of ferroptosis-related biomarkers and potential drug predictions in doxorubicin-induced cardiotoxicity.", "doi": "10.3389/fcvm.2025.1566782", "pmid": "40342975", "authors": ["Jian Yu", "Jiangtao Wang", "Xinya Liu", "Cancan Wang", "Li Wu", "Yuanming Zhang"], "journal": "Frontiers in cardiovascular medicine", "year": 2025, "abstract": "Background: Doxorubicin-induced cardiotoxicity (DIC) significantly impacts the survival and prognosis of cancer patients. Ferroptosis is involved in the pathogenesis of DIC, but its specific mechanisms remain unclear. This study aims to explore key genes of ferroptosis in DIC and potential therapeutic drugs using various bioinformatics methods.\nMethods: This study obtained the GSE106297 and GSE157282 datasets from the GEO database, conducted differential gene expression screening and GSEA enrichment analysis using R software. Subsequently obtained ferroptosis-related genes from FerrDb V2, Genecards, Geneontology, and GSEA databases, performed GO and KEGG enrichment analysis after intersecting them with the differentially expressed genes using a Venn diagram. Utilized LASSO regression, SVM-RFE, and RF algorithms to identify key genes, followed by validation using external datasets (GSE232331, GSE230638) and ROC curve plotting to determine the diagnostic value of key genes. Further validation of the expression levels of key genes were conducted through the establishment of a cell damage model. Constructed an mRNA-miRNA-lncRNA network diagram, and performed immune cell composition analysis using CIBERSORT. Finally, predicted potential drugs for key genes using the DSigDB database.\nResults: We obtained 119 genes after intersecting 1380 Differentially Expressed Genes (DEGs) with Ferroptosis-Related Genes (FRGs). Three key genes (KLHDC3, NDRG1, SPHK1) were identified through further analysis using LASSO, SAM-RFE and RF. The ROC analysis demonstrated that KLHDC3 and NDRG1 have significant diagnostic value, and qRT-PCR verification results also showed statistical significance. We constructed miRNA-lncRNA networks by identifying target miRNAs for KLHDC3 (hsa-miR-24-3p, hsa-miR-486-3p, hsa-miR-214-3p) and NDRG1 (hsa-miR-4510, hsa-miR-182-5p, hsa-miR-96-5p). Immunoinfiltration analysis revealed the relationship between KLHDC3, NDRG1 and immune cells. Anisomycin emerges as a promising small molecule drug for treating DIC, exhibiting good relative binding with KLHDC3 and NDRG1.\nConclusion: KLHDC3 and NDRG1 serve as ferroptosis biomarkers implicated in DIC and demonstrate good diagnostic value. In addition, anisomycin may also be a potential drug for treating DIC.", "claims": [{"id": "claim-method", "statement": "We identified key genes among the DE-FRGs using three machine learning algorithms: Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Random Forest (RF).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40342975", "locator": "sections[8] \"Identification of key genes\" paragraph 2", "quote": "We identified key genes among the DE-FRGs using three machine learning algorithms: Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE) and Random Forest (RF)."}], "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.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40342975", "locator": "sections[28] \"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."}], "state": "complete"}, {"id": "claim-target-result", "statement": "KLHDC3 and NDRG1 serve as ferroptosis biomarkers implicated in DIC and demonstrate good diagnostic value.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40342975", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "KLHDC3 and NDRG1 serve as ferroptosis biomarkers implicated in DIC and demonstrate good diagnostic value."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "KLHDC3 and NDRG1 serve as ferroptosis biomarkers implicated in DIC and demonstrate good diagnostic value.", "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:40342975"], "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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