Development and validation of a DNA damage repair-related gene-based prediction model for the prognosis of lung adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38249902", "kind": "science-record", "revision": 4, "content_hash": "sha256:650983b64cc151c8c7c7d72183c36f2f0f7659303ef2977b223ecf4b7b4998f4", "created_at": "2026-11-01T19:20:09Z", "updated_at": "2026-11-01T22:53:29Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Development and validation of a DNA damage repair-related gene-based prediction model for the prognosis of lung adenocarcinoma.", "doi": "10.21037/jtd-23-1746", "pmid": "38249902", "authors": ["Chu Qin", "Xiaodong Fan", "Xiaoyan Sai", "Bo Yin", "Shufang Zhou", "Alfredo Addeo", "Tao Bian", "Haoda Yu"], "journal": "Journal of thoracic disease", "year": 2023, "abstract": "Background: Lung cancer is the leading cause of morbidity and mortality among all cancer types, with lung adenocarcinoma (LUAD) being the most prevalent subtype. DNA damage repair (DDR)-related genes are closely associated with cancer progression and treatment, with emerging evidence highlighting their correlation with tumor development. However, the relationship between LUAD prognosis and DDR-related genes remains unclear.\nMethods: RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The GSE31210 dataset, utilized for external validation, was retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed DDR genes were identified, and a DDR-related prognostic model was established and validated using Kaplan-Meier (KM) survival analysis, time-dependent receiver operating characteristic (ROC) curves, gene set enrichment analysis (GSEA), tumor mutational burden (TMB) analysis, and immune cell infiltration. A P value of less than 0.05 was considered statistically significant.\nResults: A total of 514 patients with LUAD from TCGA database were divided into distinct subtypes to characterize the diversity within the DDR pathway. DDR-activated and DDR-suppressed subgroups showed distinct clinical characteristics, molecular characteristics, and immune profiles. Nine genes were identified as hub DDR-related genes, including CASP14, DKK1, ECT2, FLNC, HMMR, IGFBP1, KRT6A, TYMS, and FCER2. By using the expression levels of these selected genes, the corresponding risk scores for each sample was predicted. In the training group, KM survival analysis revealed that the high-risk group exhibited significantly diminished overall survival (OS) [hazard ratio (HR) =3.341, P=1.38e-08]. The corresponding area under the curve (AUC) values for the 1-year follow-up periods was 0.767, respectively. Upon validation in the external cohort, patients with higher risk scores manifested significantly reduced OS (HR =2.372, P=1.87e-03). The AUC values of the ROC curves for the 1-year OS in the validation cohort was 0.87, respectively. Moreover, advanced DDR risk score was correlated with increased TMB scores, a heightened frequency of TP53 mutations, an increased abundance of cancer-testicular antigens (CTAs), and a lower tumor immune dysfunction and exclusion (TIDE) score in patients with LUAD (P<0.05).\nConclusions: A nine-gene risk signature associated with DDR in LUAD was effectively developed, demonstrating its potential as a robust and reliable classification tool for clinical practice. This model exhibited the capability to accurately predict the prognosis and survival outcomes of LUAD patients.", "claims": [{"id": "claim-method", "statement": "The glmnet R package (The R Foundation for Statistical Computing, Vienna, Austria) was used to perform least absolute shrinkage and selection operator (LASSO) Cox regression for the purpose of identifying prognostic genes. To prevent overfitting, 10-fold cross-validation was employed to determine the penalized regularization parameter λ in the model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38249902", "locator": "sections[6] \"Establishment and validation of a prognostic model\" paragraph 2", "quote": "The glmnet R package (The R Foundation for Statistical Computing, Vienna, Austria) was used to perform least absolute shrinkage and selection operator (LASSO) Cox regression for the purpose of identifying prognostic genes. To prevent overfitting, 10-fold cross-validation was employed to determine the penalized regularization parameter λ in the model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "RNA sequencing (RNA-seq) data along with clinical information including age, clinical stage, mutations, copy number variations, days to death, vital status, and more, were obtained from TCGA database (https://portal.gdc.cancer.gov/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38249902", "locator": "sections[4] \"Data collection\" paragraph 2", "quote": "RNA sequencing (RNA-seq) data along with clinical information including age, clinical stage, mutations, copy number variations, days to death, vital status, and more, were obtained from TCGA database (https://portal.gdc.cancer.gov/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A nine-gene prognostic signature was developed using LASSO Cox regression on TCGA LUAD RNA-seq data and externally validated in the GEO GSE13213 cohort, with the high-risk group showing significantly poorer overall survival.", "kind": "quantitative", "target": {"value": 3.341, "unit": "hazard ratio", "tolerance": null, "source": "In the training group, KM survival analysis revealed that the high-risk group exhibited significantly poorer OS compared to the low-risk group (HR =3.341, P=1.38e−08, Figure 8A)."}, "evidence": [{"asset": "source:pmid:38249902", "locator": "sections[6] \"Establishment and validation of a prognostic model\" paragraph 2", "quote": "The glmnet R package (The R Foundation for Statistical Computing, Vienna, Austria) was used to perform least absolute shrinkage and selection operator (LASSO) Cox regression for the purpose of identifying prognostic genes."}, {"asset": "source:pmid:38249902", "locator": "sections[4] \"Data collection\" paragraph 2", "quote": "RNA sequencing (RNA-seq) data along with clinical information including age, clinical stage, mutations, copy number variations, days to death, vital status, and more, were obtained from TCGA database (https://portal.gdc.cancer.gov/)."}, {"asset": "source:pmid:38249902", "locator": "sections[4] \"Data collection\" paragraph 2", "quote": "Additionally, the GSE13213 dataset retrieved containing 117 LUAD samples from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/) was utilized for external validation."}, {"asset": "source:pmid:38249902", "locator": "sections[15] \"Construction and validation of the prognostic DDR-related gene pair signature\" paragraph 6", "quote": "In the training group, KM survival analysis revealed that the high-risk group exhibited significantly poorer OS compared to the low-risk group (HR =3.341, P=1.38e−08, Figure 8A)."}, {"asset": "source:pmid:38249902", "locator": "sections[15] \"Construction and validation of the prognostic DDR-related gene pair signature\" paragraph 8", "quote": "In the external validation cohort (GSE13213), consistent with the previous findings, patients with higher risk scores exhibited significantly shorter OS compared to those with lower risk scores (Figure 9A)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "A nine-gene prognostic signature was developed using LASSO Cox regression on TCGA LUAD RNA-seq data and externally validated in the GEO GSE13213 cohort, with the high-risk group showing significantly poorer overall survival.", "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:38249902"], "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. 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