Abstract
Gene expression regulators, such as transcription factors (TFs) and microRNAs (miRNAs), have varying regulatory targets based on the tissue and physiological state (context) within which they are expressed. While the emergence of regulator-characterizing experiments has inferred the target genes of many regulators across many contexts, methods for transferring regulator target genes across contexts are lacking. Further, regulator target gene lists frequently are not curated or have permissive inclusion criteria, impairing their use. Here, we present a method called iterative Contextual Transcriptional Activity Inference of Regulators (icTAIR) to resolve these issues. icTAIR takes a regulator’s previously-identified target gene list and combines it with gene expression data from a context, quantifying that regulator’s activity for that context. It then calculates the correlation between each listed target gene’s expression and the quantitative score of regulatory activity, removes the uncorrelated genes from the list, and iterates the process until it derives a stable list of refined target genes. To validate and demonstrate icTAIR’s power, we use it to refine the MSigDB c3 database of TF, miRNA and unclassified motif target gene lists for breast cancer. We then use its output for survival analysis with clinicopathological multivariable adjustment in 7 independent breast cancer datasets covering 3,430 patients. We uncover many novel prognostic regulators that were obscured prior to refinement, in particular NFY, and offer a detailed look at the composition and relationships among the breast cancer prognostic regulome. We anticipate icTAIR will be of general use in contextually refining regulator target genes for discoveries across many contexts. The icTAIR algorithm can be downloaded from https://github.com/icTAIR.
Highlights
The major gene expression regulators, DNA-binding transcription factors (TFs) and mRNAbinding microRNAs, have long been known to play critical roles in cellular physiology and pathophysiology, especially cancer [1,2,3,4]
Regulator Target Gene Refinement and Breast Cancer unclassified regulatory motif target gene lists was downloaded from the GSEA repository
The Encyclopedia of DNA Elements (ENCODE) [7] project has and continues to conduct Chromatin ImmunoPrecipitation followed by Sequencing (ChIP-Seq) studies to explore genomic regions of TF binding, from which TF regulatory target genes can be inferred [8]; many groups have conducted similar experiments leading to databases of TFs and their targets, such as TRANSFAC [9,10,11], ReMap [12] and ChEA [13]
Summary
The major gene expression regulators, DNA-binding transcription factors (TFs) and mRNAbinding microRNAs (miRNAs), have long been known to play critical roles in cellular physiology and pathophysiology, especially cancer [1,2,3,4]. By modulating transcription (TFs) or complementarily binding to the 3’ UTR of mRNA transcripts and decreasing or silencing their expression (miRNAs), they affect the cellular state by the integration of the induced changes in protein levels of their regulatory targets Far from monotonous, their targets and functions vary based on the tissue and cellular state (hereafter referred to as the “context”) within which they are expressed. On the miRNA side, both experimental techniques (knockdowns or inductions, combined with gene expression analysis) and computational methods, like miRanda [14], PicTar [15], PITA [16], RNAhybrid [17], and TargetScan [18], have led to the miRBase [19], TarBase [20,21,22,23], and miRTarBase [24, 25] databases of miRNAs and their predicted targets, among others Still more databases, such as the Molecular Signatures Data Base (MSigDB) [26, 27], compile results across regulators, contexts and other databases
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