Identification of ferroptosis-related genes and predicted overall survival in patients with burns.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36700031", "kind": "science-record", "revision": 3, "content_hash": "sha256:d0079f49fd3a8e84fbe0630a7f85297b340f5bacce521daee54771797bf40546", "created_at": "2026-11-01T19:01:15Z", "updated_at": "2026-11-01T22:08:38Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of ferroptosis-related genes and predicted overall survival in patients with burns.", "doi": "10.3389/fsurg.2022.1060036", "pmid": "36700031", "authors": ["Mingjian Zhao", "Yetong Zhang", "Hongliang Zhao"], "journal": "Frontiers in surgery", "year": 2022, "abstract": "Introduction: Burns are a common trauma associated with considerable mortality and morbidity. Although a lot is known regarding burns' pathogenesis, the involvement of ferroptosis is uncertain. Here, we aimed to explore vital ferroptosis-related genes and molecules in burns, through bioinformatics analysis, to uncover new effective therapeutic targets.\nMethods: The FerrDb database was used to acquire ferroptosis-related genes and GSE19743 was downloaded from Gene Expression Omnibus (GEO), a dataset with analysis of control and burned individuals. Hub genes were selected with Cytoscape software, and Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted. Cox proportional hazard function and Kaplan-Meier survival analyses were implemented to screen prognosis-related genes. Additionally, the miRWalk database was used to acquire the miRNAs relevant to our hub genes function and analyzed for enrichment.\nResult: We identified 64 differentially expressed genes and through the intersection with ferroptosis-related genes, 10 were selected as hub genes. GO analysis revealed that the hub genes' most enriched activities were response to oxidative stress, pyridine-containing compound metabolic processes, and reactive oxygen species metabolic processes. KEGG pathways' analysis showed that these overlapped genes were enriched in several pathways, namely, in VEGF signaling. Furthermore, the molecular miRNA functions significantly enriched were signal transduction and cell communication, namely, the biological pathways of the glypican pathway and the ErbB receptor signaling network. SLC40A1 and GPT2 genes were found to be associated with overall survival, suggesting an important role in burn prognosis.\nDiscussion: This study may improve our understanding of the underlying burn mechanisms and provide a new direction for the prevention of poor outcomes, advances in burns treatment, and drug development.", "claims": [{"id": "claim-method", "statement": "Kaplan–Meier survival analysis and Cox proportional hazards regression analysis were performed with R packages “survival” and “survminer,” respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36700031", "locator": "sections[7] \"Construction of the prognostic signature of FDEGs in burns\" paragraph 2", "quote": "Kaplan–Meier survival analysis and Cox proportional hazards regression analysis were performed with R packages “survival” and “survminer,” respectively."}], "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 here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19743.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36700031", "locator": "sections[18] \"Data availability statement\" 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 here: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE19743."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Based on these results, we constructed a multivariate Cox proportional hazard regression model, and SLC40A1 [HR = 0.218 (0.067–0.708), P = 0.011] and GPT2 [HR = 0.542 (0.366–0.805), P = 0.002] were determined as biomarkers correlated to prognostic (Figure 6, Tables 3, 4).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36700031", "locator": "sections[14] \"Construction of the prognostic signature of FDEGs in burn\" paragraph 2", "quote": "Based on these results, we constructed a multivariate Cox proportional hazard regression model, and SLC40A1 [HR = 0.218 (0.067–0.708), P = 0.011] and GPT2 [HR = 0.542 (0.366–0.805), P = 0.002] were determined as biomarkers correlated to prognostic (Figure 6, Tables 3, 4)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Based on these results, we constructed a multivariate Cox proportional hazard regression model, and SLC40A1 [HR = 0.218 (0.067–0.708), P = 0.011] and GPT2 [HR = 0.542 (0.366–0.805), P = 0.002] were determined as biomarkers correlated to prognostic (Figure 6, Tables 3, 4).", "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:36700031"], "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:GSE19743"], "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:GSE19743"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36700031", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fsurg.2022.1060036", "sha256": "sha256:525cd8005c39fb3800ea19e839b161d98ccf21394a6e1afb5ab36d8b71b9945e", "bytes": 24828, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fsurg.2022.1060036", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9869674", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE19743", "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=GSE19743", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE19743", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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