Abstract
BackgroundGenetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. In many cancers, alteration of TFs results in aberrant activity of them without changing their gene expression level. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, however, it is still challenge to reveal the activity change of TFs.ResultsHere we propose a method, called REACTIN (REgulatory ACTivity INference), which integrates TF binding data with gene expression data to identify TFs with significantly differential activity between disease and normal samples. REACTIN successfully detect differential activity of estrogen receptor (ER) between ER+ and ER- samples in 10 breast cancer datasets. When applied to compare tumor and normal breast samples, it reveals TFs that are critical for carcinogenesis of breast cancer. Moreover, Reaction can be utilized to identify transcriptional programs that are predictive to patient survival time of breast cancer patients.ConclusionsREACTIN provides a useful tool to investigate regulatory programs underlying a biological process providing the related case and control gene expression data. Considering the enormous amount of cancer gene expression data and the increasingly accumulating ChIP-seq data, we expect wide application of REACTIN for revealing the regulatory mechanisms of various diseases.
Highlights
Genetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers
Using breast cancer as an example, we demonstrated that the Regulatory activity inference (REACTIN) algorithm can be used to investigate regulatory mechanisms governing different breast cancer subtypes by comparison with normal tissues, in addition to identifying TF activity associated with patient survival in their activities
Overview of REACTIN algorithm for TF activity inference To investigate the regulatory mechanism underlying a specific cancer type, we developed a method called REACTIN (REgulatory ACTivity INference) to infer TFs that show significantly differential activity in the tumor samples versus the normal controls
Summary
Genetic alterations of transcription factors (TFs) have been implicated in the tumorigenesis of cancers. Gene expression data from microarray or RNA-seq experiments can capture the expression change of genes, it is still challenge to reveal the activity change of TFs. Transcription factors (TFs) are a family of proteins that regulate gene expression via binding to specific DNA sequences [1], accounting for 10% of genes in human genome [2,3]. Mutation of P53 gene is known to be a driving event for carcinogenesis in many cancer types [19,20], in most cases there is no significant P53 expression difference between tumor and normal samples: mutation abolishes its transcriptional activity by impacting its DNA binding capacity or protein stability without changing its mRNA expression level [21,22,23]
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