Identification of a novel oxidative stress-related prognostic model in lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36467027", "kind": "science-record", "revision": 3, "content_hash": "sha256:d35921a2e156606cada7b59a1eb992e4042c7fd4bdae619d3e0acdae49140746", "created_at": "2026-11-01T19:00:57Z", "updated_at": "2026-11-01T22:08:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of a novel oxidative stress-related prognostic model in lung adenocarcinoma.", "doi": "10.3389/fphar.2022.1030062", "pmid": "36467027", "authors": ["Yifan Zhu", "Quanying Tang", "Weibo Cao", "Ning Zhou", "Xin Jin", "Zuoqing Song", "Lingling Zu", "Song Xu"], "journal": "Frontiers in pharmacology", "year": 2022, "abstract": "Background: Oxidative stress (OxS) participates in a variety of biological processes, and is considered to be related to the occurrence and progression of many tumors; however, the potential diagnostic value of OxS in lung cancer remains unclear. Methods: The clinicopathological and transcriptome data for lung adenocarcinoma (LUAD) were collected from TCGA and GEO database. LASSO regression was used to construct a prognostic risk model. The prognostic significance of the OxS-related genes was explored using a Kaplan-Meier plotter database. The prediction performance of the risk model was shown in both the TCGA and GSE68465 cohorts. The qRT-PCR was performed to explore the expression of genes. CCK-8, Edu and transwell assays were conducted to analyze the role of CAT on cell proliferation migration and invasion in lung cancer. Immune infiltration was evaluated by CIBERSORT and mutational landscape was displayed in the TCGA database. Moreover, the relationship between risk score with drug sensitivity was investigated by pRRophetic. Results: We identified a prognosis related risk model based on a four OxS gene signature in LUAD, including CYP2D6, FM O 3, CAT, and GAPDH. The survival analysis and ROC curve indicated good predictive power of the model in both the TCGA and GEO cohorts. LUAD patients in the high-risk group had a shorter OS compared to the low-risk group. QRT-PCR result showed that the expression of four genes was consistent with previous analysis in cell lines. Moreover, overexpression of CAT could decrease the proliferation, invasion and migration of lung cancer cells. The Cox regression analysis showed that the risk score could be used as an independent prognostic factor for OS. LUAD patients in the high-risk score group exhibited a higher tumor mutation burden and risk score were closely related to tumor associated immune cell infiltration, as well as the expression of immune checkpoint molecules. Both the high- and low-risk groups have significant differences in sensitivity to some common chemotherapy drugs, such as Paclitaxel, Docetaxel, and Vinblastine, which may contribute to clinical treatment decisions. Conclusion: We established a robust OxS-related prognostic model, which may contribute to individualized immunotherapeutic strategies in LUAD.", "claims": [{"id": "claim-method", "statement": "The least absolute shrinkage and selection operator (LASSO) regression was conducted to construct the optimal prognostic risk model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36467027", "locator": "sections[5] \"Construction of an oxidative stress-related risk prognostic model\" paragraph 2", "quote": "The least absolute shrinkage and selection operator (LASSO) regression was conducted to construct the optimal prognostic risk model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Publicly available datasets were analyzed in this study. This data can be found here: The Cancer Genome Atlas https://portal.gdc.cancer.gov/Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/The Kaplan Meier plotter http://kmplot.com/analysis/CIBERSORT https://cibersortx.stanford.edu/GDSC www.cancerrxgene.org/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36467027", "locator": "sections[25] \"Data availability statement\" paragraph 2", "quote": "Publicly available datasets were analyzed in this study. This data can be found here: The Cancer Genome Atlas https://portal.gdc.cancer.gov/Gene Expression Omnibus https://www.ncbi.nlm.nih.gov/geo/The Kaplan Meier plotter http://kmplot.com/analysis/CIBERSORT https://cibersortx.stanford.edu/GDSC www.cancerrxgene.org/."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A LASSO-based four-gene oxidative-stress prognostic model separated LUAD patients by overall-survival risk and achieved 1-, 3-, and 5-year AUCs of 0.64, 0.67, and 0.61 in TCGA and 0.63, 0.51, and 0.63 in GSE68465.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36467027", "locator": "sections[18] \"Establishment of a prognostic risk score model based on OxS-related genes\" paragraph 4", "quote": "The AUC at 1, 3, and 5 years achieved 0.64, 0.67, 0.61, and 0.63, 0.51, 0.63, respectively for the TCGA and GSE68465 cohort (Figures 3F,H)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A LASSO-based four-gene oxidative-stress prognostic model separated LUAD patients by overall-survival risk and achieved 1-, 3-, and 5-year AUCs of 0.64, 0.67, and 0.61 in TCGA and 0.63, 0.51, and 0.63 in GSE68465.", "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:36467027"], "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:GSE68465"], "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:GSE68465"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:36467027", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fphar.2022.1030062", "sha256": "sha256:1bc77980550413fff61b9e92efc9980ce156434f6d2114452afd9c0baf86b45b", "bytes": 34663, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fphar.2022.1030062", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9715759", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE68465", "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=GSE68465", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE68465", "version": null, "note": "Publicly available datasets were analyzed in this study. 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