Identification of molecular subgroups and establishment of risk model based on the response to oxidative stress to predict overall survival of patients with lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37689745", "kind": "science-record", "revision": 3, "content_hash": "sha256:58b99a70586fe05652e8ddf600eecb9b2e6e45e49dc9f047810f5ff1f0d00e71", "created_at": "2026-11-01T19:02:29Z", "updated_at": "2026-11-01T22:10:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of molecular subgroups and establishment of risk model based on the response to oxidative stress to predict overall survival of patients with lung adenocarcinoma.", "doi": "10.1186/s40001-023-01290-5", "pmid": "37689745", "authors": ["Linzhuang Liu", "Qinghua Hou", "Baorong Chen", "Xiyi Lai", "Hanwen Wang", "Haozhen Liu", "Liusheng Wu", "Sheng Liu", "Kelin Luo", "Jixian Liu"], "journal": "European journal of medical research", "year": 2023, "abstract": "OBJECTIVE: Oxidative stress is associated with the occurrence and development of lung cancer. However, the specific association between lung cancer and oxidative stress is unclear. This study aimed to investigate the role of oxidative stress in the progression and prognosis of lung adenocarcinoma (LUAD).\nMETHODS: The gene expression profiles and corresponding clinical information were collected from GEO and TCGA databases. Differentially expressed oxidative stress-related genes (OSRGs) were identified between normal and tumor samples. Consensus clustering was applied to identify oxidative stress-related molecular subgroups. Functional enrichment analysis, GSEA, and GSVA were performed to investigate the potential mechanisms. xCell was used to assess the immune status of the subgroups. A risk model was developed by the LASSO algorithm and validated using TCGA-LUAD, GSE13213, and GSE30219 datasets.\nRESULTS: A total of 40 differentially expressed OSRGs and two oxidative stress-associated subgroups were identified. Enrichment analysis revealed that cell cycle-, inflammation- and oxidative stress-related pathways varied significantly in the two subgroups. Furthermore, a risk model was developed and validated based on the OSRGs, and findings indicated that the risk model exhibits good prediction and diagnosis values for LUAD patients.\nCONCLUSION: The risk model based on the oxidative stress could act as an effective prognostic tool for LUAD patients. Our findings provided novel genetic biomarkers for prognosis prediction and personalized clinical treatment for LUAD patients.", "claims": [{"id": "claim-method", "statement": "Based on the DEOSRGs, we performed the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a risk score model using the “glmnet” package (4.1.7) of R.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37689745", "locator": "sections[8] \"Construction and assessment of the risk prognostic model based on DEOSRGs\" paragraph 2", "quote": "Based on the DEOSRGs, we performed the least absolute shrinkage and selection operator (LASSO) regression analysis to construct a risk score model using the “glmnet” package (4.1.7) of R."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data come from the TCGA database (https://portal.gdc.cancer.gov/) and the GEO database (https://www.ncbi.nlm.nih.gov/geo).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37689745", "locator": "sections[29] \"notes\" paragraph 1", "quote": "The data come from the TCGA database (https://portal.gdc.cancer.gov/) and the GEO database (https://www.ncbi.nlm.nih.gov/geo)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A risk model was developed by the LASSO algorithm and validated using TCGA-LUAD, GSE13213, and GSE30219 datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37689745", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "A risk model was developed by the LASSO algorithm and validated using TCGA-LUAD, GSE13213, and GSE30219 datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A risk model was developed by the LASSO algorithm and validated using TCGA-LUAD, GSE13213, and GSE30219 datasets.", "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:37689745"], "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:GSE13213"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "blocked", "blocked_on": "method:group_comparison_wilcoxon", "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:GSE13213"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:run_analysis blocked on method:group_comparison_wilcoxon"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37689745", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": 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