4.3 Article

Pan-cancer predictions of transcription factors mediating aberrant DNA methylation

期刊

EPIGENETICS & CHROMATIN
卷 15, 期 1, 页码 -

出版社

BMC
DOI: 10.1186/s13072-022-00443-w

关键词

Bioinformatics; DNA methylation; Cancer; Transcription factors

资金

  1. Plan Cancer Systems Biology from the ITMO Cancer AVIESAN (French National Alliance for Life Sciences Health)
  2. initiative of excellence IDEX-Unistra from the French national programme Investment for the future
  3. Ligue Nationale Contre le Cancer

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This study integrated a massive amount of genomic data to predict transcription factors driving aberrant DNA methylation in 13 cancer types, and validated the role of some transcription factors in breast cancer.
Background Aberrant DNA methylation is a hallmark of cancer cells. However, the mechanisms underlying changes in DNA methylation remain elusive. Transcription factors initially thought to be repressed from binding by DNA methylation, have recently emerged as being able to shape DNA methylation patterns. Results Here, we integrated the massive amount of data available from The Cancer Genome Atlas to predict transcription factors driving aberrant DNA methylation in 13 cancer types. We identified differentially methylated regions between cancer and matching healthy samples, searched for transcription factor motifs enriched in those regions and selected transcription factors with corresponding changes in gene expression. We predict transcription factors known to be involved in cancer as well as novel candidates to drive hypo-methylated regions such as FOXA1 and GATA3 in breast cancer, FOXA1 and TWIST1 in prostate cancer and NFE2L2 in lung cancer. We also predict transcription factors that lead to hyper-methylated regions upon transcription factor loss such as EGR1 in several cancer types. Finally, we validate that FOXA1 and GATA3 mediate hypo-methylated regions in breast cancer cells. Conclusion Our work highlights the importance of some transcription factors as upstream regulators shaping DNA methylation patterns in cancer.

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