Identification of a small optimal subset of CpG sites as bio-markers from high-throughput DNA methylation profiles

Background: DNA methylation patterns have been shown to significantly correlate with different tissue types and disease states. High-throughput methylation arrays enable large-scale DNA methylation analysis to identify informative DNA methylation biomarkers. The identification of disease-specific methylation signatures is of fundamental and practical interest for risk assessment, diagnosis, and prognosis of diseases. Results: Using published high-throughput DNA methylation data, a two-stage feature selection method was developed to select a small optimal subset of DNA methylation features to precisely classify two sample groups. With this approach, a small number of CpG sites were highly sensitive and specific in distinguishing lung cancer tissue samples from normal lung tissue samples. Conclusion: This study shows that it is feasible to identify DNA methylation biomarkers from high-throughput DNA methylation profiles and that a small number of signature CpG sites can suffice to class...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Hailong Meng, Edward Lenn Murrelle, Guoya Li
• Keywords: BMCBI 2008, DNA Methylation, DNA Methylation Biomarkers, DNA Methylation Patterns