Construction and validation of a prognostic model based on oxidative stress-related genes in non-small cell lung cancer (NSCLC): predicting patient outcomes and therapy responses.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39669999", "kind": "science-record", "revision": 3, "content_hash": "sha256:7c39d1ff55e2b6f7de602d22b91f0e2d69101f5c6328052a407f54401fb267ec", "created_at": "2026-11-01T20:29:57Z", "updated_at": "2026-11-01T22:13:19Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Construction and validation of a prognostic model based on oxidative stress-related genes in non-small cell lung cancer (NSCLC): predicting patient outcomes and therapy responses.", "doi": "10.21037/tlcr-24-888", "pmid": "39669999", "authors": ["Dongfeng Sun", "Jie Lu", "Wenhua Zhao", "Xiaozheng Chen", "Changyan Xiao", "Feng Hua", "Per Hydbring", "Esteban C Gabazza", "Alfredo Tartarone", "Xiaoming Zhao", "Wenfeng Yang"], "journal": "Translational lung cancer research", "year": 2024, "abstract": "Background: Non-small cell lung cancer (NSCLC) is a significant health concern. The prognostic value of oxidative stress (OS)-related genes in NSCLC remains unclear. The study aimed to explore the prognostic significance of OS-genes in NSCLC using extensive datasets from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO).\nMethods: The research used the expression data and clinical information of NSCLC patients to develop a risk-score model. A total of 74 OS-related differentially expressed genes (DEGs) were identified by comparing NSCLC and control samples. Univariate Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were employed to identify the prognostic biomarkers. A risk-score model was constructed and validated with receiver operating characteristic (ROC) curves in TCGA and GSE72094 datasets. The model's accuracy was further verified by univariate and multivariate Cox regression.\nResults: The identified biomarkers, including lactate dehydrogenase A (LDHA), protein tyrosine phosphatase receptor type N (PTPRN), and transient receptor potential cation channel subfamily A (TRPA1) demonstrated prognostic significance in NSCLC. The risk-score model showed good predictive accuracy, with 1-year area under the curves (AUC) of 0.661, 3-year AUC of 0.648, and 5-year AUC of 0.634 in the TCGA dataset, and 1-year AUC of 0.643, 3-year AUC of 0.648, and 5-year AUC of 0.662 in the GSE72094 dataset. A nomogram integrating risk score and tumor node metastasis (TNM) stage was developed. The signature effectively distinguished between patient responses to immunotherapy. High-risk groups were characterized by an immunosuppressive microenvironment and an increased tumor mutational burden (TMB), marked by a higher incidence of mutations in genes such as TP53, DCP1B, ELN, and MAGI2. Organoid drug sensitivity testing revealed that NSCLC patients with a low-risk score responded better to chemotherapy.\nConclusions: This study successfully developed a robust model for predicting patient prognosis in NSCLC, highlighting the critical prognostic value of OS-genes. These findings hold significant potential to refine treatment strategies, and enhance survival outcomes for NSCLC patients. By enabling a personalized therapeutic approach tailored to individual risk scores, this model may facilitate more precise decisions concerning immunotherapy and chemotherapy, thereby optimizing patient management and treatment efficacy.", "claims": [{"id": "claim-method", "statement": "In the initial phase, we employed the training dataset to conduct a univariate Cox regression analysis to identify OS-DEGs significantly associated with survival outcomes, using a P value <0.05 as the criterion. Thereafter, the OS-DEGs identified by the univariate Cox regression analysis were further screened by the least absolute shrinkage and selection operator (LASSO) algorithm using the “glmnet” R package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39669999", "locator": "sections[6] \"Identification of robust prognostic OS-DEGs in NSCLC\" paragraph 2", "quote": "In the initial phase, we employed the training dataset to conduct a univariate Cox regression analysis to identify OS-DEGs significantly associated with survival outcomes, using a P value <0.05 as the criterion. Thereafter, the OS-DEGs identified by the univariate Cox regression analysis were further screened by the least absolute shrinkage and selection operator (LASSO) algorithm using the “glmnet” R package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-888/dss", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39669999", "locator": "sections[28] \"Data Sharing Statement\" paragraph 2", "quote": "Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-24-888/dss"}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "", "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:39669999"], "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:GSE72094"], "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:GSE72094"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39669999", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.21037/tlcr-24-888", "sha256": 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