ArticleBMC pulmonary medicine2022
DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma.
Article in BMC pulmonary medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Development and validation of a 5mC-based prognostic model for lung adenocarcinoma survival.Cell reports. Medicine · 2026Article
- Translating tumor epigenetic subtyping into methylome network-based prognostic models in early-stage NSCLC: results from the prospective MOBIT study.Journal of translational medicine · 2026Article
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
- [Research Progress on the Role of GSDME-mediated Pyroptosis in the Treatment of Lung Cancer].Zhongguo fei ai za zhi = Chinese journal of lung cancer · 2024Review
- Article
- Identification of N7-methylguanosine related signature for prognosis and immunotherapy efficacy prediction in lung adenocarcinoma.Frontiers in medicine · 2022Article
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Authors and funding
4 authors.
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Abstract
aimsLung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis.
methodsIn the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation.
resultsOverall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis.
conclusionThe model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD.
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