Oxidative stress gene signature construction to identify subtypes and prognosis of patients with lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39640604", "kind": "science-record", "revision": 3, "content_hash": "sha256:5f3179934dbf620ce602bc4de94c544d57cf92d52c23e726412a4196f97142f2", "created_at": "2026-11-01T20:29:55Z", "updated_at": "2026-11-01T22:13:16Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Oxidative stress gene signature construction to identify subtypes and prognosis of patients with lung adenocarcinoma.", "doi": "10.1016/j.heliyon.2024.e38306", "pmid": "39640604", "authors": ["Lan Li", "Rujia Qin", "Xuefeng Wang", "Ke Cao", "Fei Lu", "Zhengting Chen", "Jingyan Gao", "Linbo Qiu", "Sisong Shu", "Han Lu", "Li Chang", "Wenhui Li"], "journal": "Heliyon", "year": 2024, "abstract": "Background: Although oxidative stress and malignancies are intimately connected, it is unknown how lung adenocarcinoma (LUAD) is affected by oxidative stress response-related genes (OSRGs).Our goal in this work was to create a genetic signature based on OSRGs that might both predict prognosis and hint to potential treatment options for LUAD.\nMethods: Clinicopathological and transcriptome information on LUAD patients was obtained from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. A model for predicting risk was created using LASSO regression. The TCGA, GSE72094, and GSE41271 cohorts all demonstrated the risk model's prediction ability. Immune cell infiltration was measured using the CIBERSORT method, and the TIDE platform was implemented to evaluate the therapeutic efficacy of immune checkpoint inhibition (ICI). Chemotherapy sensitivity was predicted using drug activity data by the Genomics of Drug Sensitivity. An investigation into gene expression was conducted using qRT-PCR. CCK-8 and transwell assays were employed to look into how DKK1 affected the migration and proliferation of LUAD cells.\nResults: A gene signature consisting of ANLN, FAM83A, DKK1, LOXL2, RHOV, IGFBP1, CCR2, GNG7, and C11orf16 was efficiently determined and used to calculate a patient-specific risk score, this functioned as a stand-alone biomarker for prediction. Correlations were found between risk scores and immune cell infiltration frequency, ICI therapy response rate, estimated chemotherapeutic drug susceptibility and autophagy-related genes.Furthermore, DKK1 knockdown reduced the ability of LUAD cells to multiply and migrate.\nConclusion: Our thorough transcriptome study of OSRGs generated a biological framework effective in forecasting outcome and responsiveness to therapy in LUAD patients.", "claims": [{"id": "claim-method", "statement": "To locate DEGs, the 'limma' package was utilized with the following criteria applied: |log 2 fold-change (FC)| > 0.5, FDR <0.05. DEGs and OSRGs were investigated using one-way Cox regression analysis. Then, multicollinearity was decreased using LASSO Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39640604", "locator": "sections[6] \"Developing and verifying the prognostic OSRG signature\" paragraph 2", "quote": "To locate DEGs, the 'limma' package was utilized with the following criteria applied: |log 2 fold-change (FC)| > 0.5, FDR <0.05. DEGs and OSRGs were investigated using one-way Cox regression analysis. Then, multicollinearity was decreased using LASSO Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of the present study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/). Data will be made available on request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39640604", "locator": "sections[25] \"Data availability\" paragraph 2", "quote": "The data that support the findings of the present study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/). Data will be made available on request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In training set TCGA-LUAD data, the prognosis was worse for high-risk cohort (P < 0.001) (Fig. 5A), and expected AUC values of data sets at 1, 3, and 5 years were 0.760, 0.714, and 0.660, correspondingly (Fig. 5B), indicating the outstanding predictive value of the model.", "kind": "quantitative", "target": {"value": 0.001, "unit": "p-value", "tolerance": null, "source": "In training set TCGA-LUAD data, the prognosis was worse for high-risk cohort (P < 0.001) (Fig. 5A), and expected AUC values of data sets at 1, 3, and 5 years were 0.760, 0.714, and 0.660, correspondingly (Fig. 5B), indicating the outstanding predictive value of the model."}, "evidence": [{"asset": "source:pmid:39640604", "locator": "sections[17] \"The OSRG signature can predict patient prognosis\" paragraph 2", "quote": "In training set TCGA-LUAD data, the prognosis was worse for high-risk cohort (P < 0.001) (Fig. 5A), and expected AUC values of data sets at 1, 3, and 5 years were 0.760, 0.714, and 0.660, correspondingly (Fig. 5B), indicating the outstanding predictive value of the model."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In training set TCGA-LUAD data, the prognosis was worse for high-risk cohort (P < 0.001) (Fig. 5A), and expected AUC values of data sets at 1, 3, and 5 years were 0.760, 0.714, and 0.660, correspondingly (Fig. 5B), indicating the outstanding predictive value of the model.", "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:39640604"], "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:GSE41271"], "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:GSE41271"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39640604", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.heliyon.2024.e38306", "sha256": "sha256:462a4282b692cf653d463702903bc1039665cacbfee43ec3f728c41da3e4b48a", "bytes": 54005, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.heliyon.2024.e38306", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11619975", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE41271", "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=GSE41271", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE41271", "version": null, "note": "The data that support the findings of the present study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/). Data will be made available on request.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The data that support the findings of the present study are openly available in GEO (https://www.ncbi.nlm.nih.gov/geo/) and TCGA (https://portal.gdc.cancer.gov/). 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