ArticleBioData mining2018
Integrative analysis of gene expression and methylation data for breast cancer cell lines.
Article in BioData mining, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Integrated Analysis of DNA Methylation and Gene Expression Profiles in a Rat Model of Osteoarthritis.International journal of molecular sciences · 2024Article
- Genome-wide methylomic regulation of multiscale gene networks in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2023Article
- Thorough statistical analyses of breast cancer co-methylation patterns.BMC genomic data · 2022Article
- Within-sample co-methylation patterns in normal tissues.BioData mining · 2019Article
- Hemimethylation Patterns in Breast Cancer Cell Lines.Cancer informatics · 2019Article
- Combining DNA methylation and RNA sequencing data of cancer for supervised knowledge extraction.BioData mining · 2018Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe deadly costs of cancer and necessity for an accurate method of early cancer detection have demanded the identification of genetic and epigenetic factors associated with cancer. DNA methylation, an epigenetic event, plays an important role in cancer susceptibility. In this paper, we use DNA methylation and gene expression data integration and pathway analysis to further explore and understand the complex relationship between methylation and gene expression.
resultsThrough linear modeling and analysis of variance, we obtain genes that show a significant correlation between methylation and gene expression. We then examine the functions and relationships of these genes using bioinformatic tools and databases. In particular, using ConsensusPathDB, we analyze the networks of statistically significant genes to identify hub genes, genes with a large number of links to other genes. We identify eight major hub genes, all in strong association with cancer susceptibility. Through further analysis of the function, gene expression level, and methylation level of these hub genes, we conclude that they are novel potential biomarkers for breast cancer.
conclusionsOur findings have various implications for cancer screening, early detection methods, and potential novel treatments for cancer. Researchers can also use our results to develop more effective methods for cancer study.
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