ArticleJournal of translational medicine2024
Novel genome-wide DNA methylation profiling reveals distinct epigenetic landscape, prognostic model and cellular composition of early-stage lung adenocarcinoma.
Article in Journal of translational medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Integrated genetic and epigenetic analysis identifies that rs939408 affects non-smoking lung adenocarcinoma risk by modulating the DNA methylation of LRRC2.Cell death & disease · 2025Article
- Methylation-regulated tumor suppressor geneTranslational cancer research · 2025Article
- Integration of single-cell and bulk RNA sequencing to identify a distinct tumor stem cells and construct a novel prognostic signature for evaluating prognosis and immunotherapy in LUAD.Journal of translational medicine · 2025Article
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8 authors.
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Abstract
backgroundLung adenocarcinoma (LUAD) has been a leading cause of cancer-related mortality worldwide. Early intervention can significantly improve prognosis. DNA methylation could occur in the early stage of tumor. Comprehensive understanding the epigenetic landscape of early-stage LUAD is crucial in understanding tumorigenesis.
methodsEnzymatic methyl sequencing (EM-seq) was performed on 23 tumors and paired normal tissue to reveal distinct epigenetic landscape, for compared with The Cancer Genome Atlas (TCGA) 450K methylation microarray data. Then, an integrative analysis was performed combined with TCGA LUAD RNA-seq data to identify significant differential methylated and expressed genes. Subsequently, the prognostic risk model was constructed and cellular composition was analyzed.
resultsMethylome analysis of EM-seq comparing tumor and normal tissues identified 25 million cytosine-phosphate-guanine (CpG) sites and 30,187 differentially methylated regions (DMR) with a greater number of untraditional types. EM-seq identified a significantly higher number of CpG sites and DMRs compared to the 450K microarray. By integrating the differentially methylated genes (DMGs) with LUAD-related differentially expressed genes (DEGs) from the TCGA database, we constructed prognostic model based on six differentially methylated-expressed genes (MEGs) and verified our prognostic model in GSE13213 and GSE42127 dataset. Finally, cell deconvolution based on the in-house EM-seq methylation profile was used to estimate cellular composition of early-stage LUAD.
conclusionsThis study firstly delves into novel pattern of epigenomic DNA methylation and provides a multidimensional analysis of the role of DNA methylation revealed by EM-seq in early-stage LUAD, providing distinctive insights into its potential epigenetic mechanisms.
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