ArticleGenetics and molecular biology2020
Comprehensive analysis of gene expression and DNA methylation data identifies potential biomarkers and functional epigenetic modules for lung adenocarcinoma.
Article in Genetics and molecular biology, 2020. 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.
- Article
- Integrative Analysis of DNA Methylation and Gene Expression Data Identifies Potential Biomarkers and Functional Epigenetic Modules for SARS-CoV-2.Biochemical genetics · 2023Article
- Role of methylation-related genesAnnals of translational medicine · 2022Article
- A Deep Learning-Based Framework for Supporting Clinical Diagnosis of Glioblastoma Subtypes.Frontiers in genetics · 2022Article
- circ-ANXA7 facilitates lung adenocarcinoma progression via miR-331/LAD1 axis.Cancer cell international · 2021Article
- The bioinformatics analysis of RIOX2 gene in lung adenocarcinoma and squamous cell carcinoma.PloS one · 2021Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Lung cancer has one of the highest mortality rates of malignant neoplasms. Lung adenocarcinoma (LUAD) is one of the most common types of lung cancer. DNA methylation is more stable than gene expression and could be used as a biomarker for early tumor diagnosis. This study is aimed to screen potential DNA methylation signatures to facilitate the diagnosis and prognosis of LUAD and integrate gene expression and DNA methylation data of LUAD to identify functional epigenetic modules. We systematically integrated gene expression and DNA methylation data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), bioinformatic models and algorithms were implemented to identify signatures and functional modules for LUAD. Three promising diagnostic and five potential prognostic signatures for LUAD were screened by rigorous filtration, and our tumor-normal classifier and prognostic model were validated in two separate data sets. Additionally, we identified functional epigenetic modules in the TCGA LUAD dataset and GEO independent validation data set. Interestingly, the MUC1 module was identified in both datasets. The potential biomarkers for the diagnosis and prognosis of LUAD are expected to be further verified in clinical practice to aid in the diagnosis and treatment of LUAD.
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Registered trials
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