ArticleOncology letters2019
A novel CpG island methylation panel predicts survival in lung adenocarcinomas.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 9 citations in OpenAlex.
- A Computational Recognition Analysis of Promising Prognostic Biomarkers in Breast, Colon and Lung Cancer Patients.International journal of molecular sciences · 2025Article
- Environmental Factor Index (EFI): A Novel Approach to Measure the Strength of Environmental Influence on DNA Methylation in Identical Twins.Epigenomes · 2024Article
- Ribosomal protein RPL39L is an efficiency factor in the cotranslational folding of a subset of proteins with alpha helical domains.Nucleic acids research · 2024Article
- Genome-wide promoter methylation profiling in a cellular model of melanoma progression reveals markers of malignancy and metastasis that predict melanoma survival.Clinical epigenetics · 2022Article
- Identification and validation of novel DNA methylation markers for early diagnosis of lung adenocarcinoma.Molecular oncology · 2020Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The lack of clinically useful biomarkers compromise the personalized management of lung adenocarcinomas (ADCs); epigenetic events and DNA methylation in particular have exhibited potential value as biomarkers. By comparing genome-wide DNA methylation data of paired lung ADCs and normal tissues from 6 public datasets, cancer-specific CpG island (CGI) methylation changes were identified with a pre-specified criterion. Correlations between DNA methylation and expression data for each gene were assessed by Pearson correlation analysis. A prognostically relevant CGI methylation signature was constructed by risk-score analysis, and was validated using a training-validation approach. Survival data were analyzed by log-rank test and Cox regression model. In total, 134 lung ADC-specific CGI CpGs were identified, among which, a panel of 9 CGI loci were selected as prognostic candidates, and were used to construct a risk-score signature. The novel CGI methylation signature was identified to classify distinct prognostic subgroups across different datasets, and was demonstrated to be a potent independent prognostic factor for overall survival time of patients with lung ADCs. In addition, it was identified that cancer-specific CGI hypomethylation of
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Registered trials
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