Dataset Information

An integrated approach to uncover driver genes in breast cancer methylation genomes.

ABSTRACT:

Background

Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play "driver" roles in tumorigenesis, whereas others are only "passengers".

Results

Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene expression data to identify driver methylation aberrations of cancer. Applying this approach to breast cancer data, we identified many novel cancer driver genes and found that some of the identified driver genes were subtype-specific for basal-like, luminal-A and HER2+ subtypes of breast cancer.

Conclusion

The proposed framework proved effective in identifying cancer driver genes from genome-wide gene methylation and expression data of cancer. These results may provide new molecular targets for potential targeted and selective epigenetic therapy.

SUBMITTER: Shen X

PROVIDER: S-EPMC3620319 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

An integrated approach to uncover driver genes in breast cancer methylation genomes.

Shen Xiaopei X Li Shan S Zhang Lin L Li Hongdong H Hong Guini G Zhou Xianxiao X Zheng Tingting T Zhang Wenjing W Hao Chunxiang C Shi Tongwei T Liu Chunyang C Guo Zheng Z

PloS one 20130408 4

<h4>Background</h4>Cancer cells typically exhibit large-scale aberrant methylation of gene promoters. Some of the genes with promoter methylation alterations play "driver" roles in tumorigenesis, whereas others are only "passengers".<h4>Results</h4>Based on the assumption that promoter methylation alteration of a driver gene may lead to expression alternation of a set of genes associated with cancer pathways, we developed a computational framework for integrating promoter methylation and gene ex ...[more]

PMID: 23579546

Similar Datasets

Project description:Background: Aberrant DNA methylation occurs commonly during carcinogenesis and is of clinical value in human cancers. However, knowledge of the impact of DNA methylation changes on lung carcinogenesis and progression remains limited. Methods: Genome-wide DNA methylation profiles were surveyed in 18 pairs of tumors and adjacent normal tissues from non-small cell lung cancer (NSCLC) patients using Reduced Representation Bisulfite Sequencing (RRBS). An integrated epigenomic-transcriptomic landscape of lung cancer was depicted using the multi-omics data integration method. Results: We discovered a large number of hypermethylation events pre-marked by poised promoter in embryonic stem cells, being a hallmark of lung cancer. These hypermethylation events showed a high conservation across cancer types. Eight novel driver genes with aberrant methylation (e.g., PCDH17 and IRX1) were identified by integrated analysis of DNA methylome and transcriptome data. Methylation level of the eight genes measured by pyrosequencing can distinguish NSCLC patients from lung tissues with high sensitivity and specificity in an independent cohort. Their tumor-suppressive roles were further experimentally validated in lung cancer cells, which depend on promoter hypermethylation. Similarly, 13 methylation-driven ncRNAs (including 8 lncRNAs and 5 miRNAs) were identified, some of which were co-regulated with their host genes by the same promoter hypermethylation. Finally, by analyzing the transcription factor (TF) binding motifs, we uncovered sets of TFs driving the expression of epigenetically regulated genes and highlighted the epigenetic regulation of gene expression of TCF21 through DNA methylation of EGR1 binding motifs. Conclusions: We discovered several novel methylation driver genes of diagnostic and therapeutic relevance in lung cancer. Our findings revealed that DNA methylation in TF binding motifs regulates target gene expression by affecting the binding ability of TFs. Our study also provides a valuable epigenetic resource for identifying DNA methylation-based diagnostic biomarkers, developing cancer drugs for epigenetic therapy and studying cancer pathogenesis.

Dataset Information

An integrated approach to uncover driver genes in breast cancer methylation genomes.

Background

Results

Conclusion

Publications

An integrated approach to uncover driver genes in breast cancer methylation genomes.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets