Identification and validation of ferroptosis-related biomarkers and the related pathogenesis in precancerous lesions of gastric cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37752199", "kind": "science-record", "revision": 4, "content_hash": "sha256:684b69a1d9393947ff433015b55d094f076234a5b7c55a2c4399d6d2cd0534e5", "created_at": "2026-11-01T19:02:35Z", "updated_at": "2026-11-01T22:52:47Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of ferroptosis-related biomarkers and the related pathogenesis in precancerous lesions of gastric cancer.", "doi": "10.1038/s41598-023-43198-4", "pmid": "37752199", "authors": ["Yuhui Kuang", "Kuo Yang", "Lingkai Meng", "Yijia Mao", "Fangbiao Xu", "Huayi Liu"], "journal": "Scientific reports", "year": 2023, "abstract": "Using advanced bioinformatics techniques, we conducted an analysis of ferroptosis-related genes (FRGs) in precancerous lesions of gastric cancer (PLGC). We also investigated their connection to immune cell infiltration and diagnostic value, ultimately identifying new molecular targets that could be used for PLGC patient treatment. The Gene Expression Omnibus (GEO) and FerrDb V2 databases were used to identify FRGs. These genes were analysed via ClueGO pathways and Gene Ontology (GO) enrichment analysis, as well as single-cell dataset GSE134520 analysis. A machine learning model was applied to identify hub genes associated with ferroptosis in PLGC patients. Receiver Operating Characteristics (ROC) curve analysis was conducted to verify the diagnostic efficacy of these genes, and a PLGC diagnosis model nomogram was established based on hub genes. R software was utilized to conduct functional, pathway, gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) on the identified diagnostic genes. Hub gene expression levels and survival times in gastric cancer were analysed using online databases to determine the prognostic value of these genes. MCPcounter and single-sample gene set enrichment analysis (ssGSEA) algorithms were used to investigate the correlation between hub genes and immune cells. Finally, noncoding RNA regulatory mechanisms and transcription factor regulatory networks for hub genes were mapped using multiple databases. Eventually, we identified 23 ferroptosis-related genes in PLGC. Enrichment analyses showed that ferroptosis-related genes were closely associated with iron uptake and transport and ferroptosis in the development of PLGC. After differential analysis using machine learning algorithms, we identified four hub genes in PLGC patients, including MYB, CYB5R1, LIFR and DPP4. Consequently, we established a ferroptosis diagnosis model nomogram. GSVA and GSEA mutual verification analysis helped uncover potential regulatory mechanisms of hub genes. MCPcounter and ssGSEA analysed immune infiltration in the disease and indicated that B cells and parainflammation played an important role in disease progression. Finally, we constructed noncoding RNA regulatory networks and transcription factor regulatory networks. Our study identified ferroptosis-related diagnostic genes and therapeutic targets for PLGC, providing novel insights and a theoretical foundation for research into the molecular mechanisms, clinical diagnosis, and treatment of this disease.", "claims": [{"id": "claim-method", "statement": "This study utilized four different machine learning models, including random forests16 (RF), support vector machines17 (SVM), eXtreme Gradient Boosting18 (XGBoost), and generalized linear models19 (GLM), to help screen for core genes within the ferroptosis-related gene set.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37752199", "locator": "sections[7] \"Identification of ferroptosis-related hub genes by machine learning\" paragraph 2", "quote": "This study utilized four different machine learning models, including random forests16 (RF), support vector machines17 (SVM), eXtreme Gradient Boosting18 (XGBoost), and generalized linear models19 (GLM), to help screen for core genes within the ferroptosis-related gene set."}], "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 in the article. GSE130823 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE130823), GSE78523 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78523) and GSE134520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134520).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37752199", "locator": "sections[26] \"Data availability\" 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 in the article. GSE130823 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE130823), GSE78523 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE78523) and GSE134520 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134520)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "After differential analysis using machine learning algorithms, we identified four hub genes in PLGC patients, including MYB, CYB5R1, LIFR and DPP4.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37752199", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "After differential analysis using machine learning algorithms, we identified four hub genes in PLGC patients, including MYB, CYB5R1, LIFR and DPP4."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "After differential analysis using machine learning algorithms, we identified four hub genes in PLGC patients, including MYB, CYB5R1, LIFR and DPP4.", "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:37752199"], "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:GSE130823"], "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:GSE130823"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37752199", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1038/s41598-023-43198-4", "sha256": "sha256:bf01668aa0d486c9fdb714d73bc61805d394aceb081d306697799c49a7413e23", "bytes": 47079, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1038/s41598-023-43198-4", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10522668", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE130823", "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=GSE130823", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE130823", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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