ArticleJournal of thoracic disease2023
Development and validation of a DNA damage repair-related gene-based prediction model for the prognosis of lung adenocarcinoma.
Article in Journal of thoracic disease, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Functional roles of Keratin 6A in disease pathogenesis across cancer and skin disorders.Experimental biology and medicine (Maywood, N.J.) · 2026Review
- Interpretable machine learning model integrating CT radiomics, CTR, and clinical features for EGFR mutation prediction in ≤3 cm lung adenocarcinoma nodules.Annals of medicine · 2025Article
- PSMD14 drives lung adenocarcinoma progression through HMMR stabilization and dual activation of TGF-β/Smad and PI3K/AKT/mTOR signaling.Frontiers in immunology · 2025Article
- Exploring the molecular and immune landscape of cellular senescence in lung adenocarcinoma.Frontiers in immunology · 2024Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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. Methods: 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. Results: 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 Conclusions: 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.
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