title: Transcriptome-wide signatures of tumor stage in kidney renal clear cell carcinoma: connecting copy number variation, methylation and transcription factor activity.
abstract: BACKGROUND
  Comparative analysis of expression profiles between early and late stage cancers can help to understand cancer progression and metastasis mechanisms and to predict the clinical aggressiveness of cancer. The observed stage-dependent expression changes can be explained by genetic and epigenetic alterations as well as transcription dysregulation. Unlike genetic and epigenetic alterations, however, activity changes of transcription factors, generally occurring at the post-transcriptional or post-translational level, are hard to detect and quantify.
  
  
  METHODS
  Here we developed a statistical framework to infer the activity changes of transcription factors by simultaneously taking into account the contributions of genetic and epigenetic alterations to mRNA expression variations.
  
  
  RESULTS
  Applied to kidney renal clear cell carcinoma (KIRC), the model underscored the role of methylation as a significant contributor to stage-dependent expression alterations and identified key transcription factors as potential drivers of cancer progression.
  
  
  CONCLUSIONS
  Integrating copy number, methylation, and transcription factor activity signatures to explain stage-dependent expression alterations presented a precise and comprehensive view on the underlying mechanisms during KIRC progression.
authors: Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232 USA ; Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, TN 37232 USA.
authors: Qi
authors: Q
authors: Liu
authors: Department of Statistics, National Cheng Kung University, Tainan, 70101 Taiwan.
authors: Pei-Fang
authors: PF
authors: Su
authors: Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232 USA.
authors: Shilin
authors: S
authors: Zhao
authors: Center for Quantitative Sciences, Vanderbilt University School of Medicine, Nashville, TN 37232 USA ; Department of Cancer Biology, Vanderbilt University School of Medicine, Nashville, TN 37232 USA ; Department of Biostatistics, Vanderbilt University School of Medicine, Nashville, TN 37232 USA ; School of Life Sciences & Biotechnology, Shanghai Jiao Tong University, Shanghai, 200240 China.
authors: Yu
authors: Y
authors: Shyr
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