ArticleTranslational lung cancer research2023
APOBEC mutational signature predicts prognosis and immunotherapy response in nonsmoking patients with lung adenocarcinoma.
Article in Translational lung cancer research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Efficacy of PD-1/PD-L1 inhibitors combined with multi-targeted anti-angiogenic TKIs in advanced or metastatic NSCLC: A meta-analysis based on RCTs.Frontiers in oncology · 2026Pooled it
- Proteogenomic characterization of pulmonary large-cell neuroendocrine carcinoma identifies molecular features and therapeutic strategy.Science advances · 2026Article
- BRCA2 deficiency and replication stress drive APOBEC3-Mediated genomic instability.Nature communications · 2025Article
- FAT1 mutation-related signature predicts survival risk and tumor immunogenicity in lung adenocarcinoma.Frontiers in genetics · 2025Article
- [Mutational Signatures Analysis of Micropapillary Components and Exploration of ZNF469 Gene in Early-stage Lung Adenocarcinoma with Ground-glass Opacities].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2024Article
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6 authors.
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Abstract
Background: Lung adenocarcinoma (LUAD) is the most common type of non-small cell lung cancer (NSCLC) with poor survival in advanced stage. Nowadays the rate of nonsmoking patients has dramatically increased and may be associated with the presence of driver mutations. Better understanding of the mutation profile data of nonsmoking LUAD patients are critical to predict survival and provide greater benefits to more patients. The apolipoprotein B mRNA editing enzyme catalytic polypeptide-like (APOBEC) has been shown to play an important role in molecular tumorigenesis of NSCLC. However, the clinical relevance of APOBEC in nonsmoking LUAD remains to be understood. Methods: LUAD patients with somatic mutation and RNA sequencing data obtained from The Cancer Genome Atlas (TCGA) were assessed and screened in the Gene Expression Omnibus. Transcriptome data and mutational signatures were analyzed using R package. Then, we used the least absolute shrinkage and selection operator (LASSO) regression model to construct the APOBEC3 score (APOBEC3 score) model. The prognostic value was evaluated using Kaplan-Meier analysis. Finally, the functional enrichment analysis of differential expressed genes (DEGs) and the immune-related features were also estimated using R package. Results: By analyzing the mutational profile data of NSCLC in the TCGA database, we found that different mutation patterns existed between smoking and nonsmoking patients, and the APOBEC3 family played an important role in the mutation pattern of nonsmoking patients with LUAD. We established an APOBEC3 score and found that TCW (W = A or T) mutation counts were significantly greater in the high APOBEC3 score group than in the low APOBEC3 score group. Furthermore, there were different immune feathers and prognostic values between the high and low APOBEC3 score patients, suggesting an independent prognostic factor of APOBEC3 in nonsmoking LUAD patients. Conclusions: We established a comprehensive view of APOBEC3 mutations in nonsmoking LUAD patients. Our review provides new insights into using the APOBEC3 mutation to predict prognosis and improve the immunotherapy response for future applications.
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