Abstract

The dysregulation of gene expression is an enabling hallmark of cancer. Computational analysis of transcriptomics data from human cancer specimens, complemented with exhaustive clinical annotation, provides an opportunity to identify core regulators of the tumorigenic process. Here we exploit well-annotated clinical datasets of prostate cancer for the discovery of transcriptional regulators relevant to prostate cancer. Following this rationale, we identify Microphthalmia-associated transcription factor (MITF) as a prostate tumor suppressor among a subset of transcription factors. Importantly, we further interrogate transcriptomics and clinical data to refine MITF perturbation-based empirical assays and unveil Crystallin Alpha B (CRYAB) as an unprecedented direct target of the transcription factor that is, at least in part, responsible for its tumor-suppressive activity in prostate cancer. This evidence was supported by the enhanced prognostic potential of a signature based on the concomitant alteration of MITF and CRYAB in prostate cancer patients. In sum, our study provides proof-of-concept evidence of the potential of the bioinformatics screen of publicly available cancer patient databases as discovery platforms, and demonstrates that the MITF-CRYAB axis controls prostate cancer biology.

Highlights

  • Balanced integration of intracellular circuits operates within a normal cell to sustain physiological homeostasis

  • Bioinformatics screening identifies Microphthalmia-associated transcription factor (MITF) as a transcription factor altered in prostate cancer

  • We have recently demonstrated that the reduced expression of the transcriptional coactivator PGC1α is a causal event for metastatic PCa14

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Summary

Introduction

Balanced integration of intracellular circuits operates within a normal cell to sustain physiological homeostasis. Alterations in some, if not all, of these circuits converge in changes on gene expression, which will eventually enable the acquisition and sustenance of the hallmarks of cancer cells[1]. Transcriptomics data derived from cancer specimens have become an important resource for the classification, stratification, and molecular driver identification in tumors. Prostate cancer (PCa) research exemplifies the effort in deciphering the genomics and transcriptomics landscape of tumors, and extremely valuable data have been generated[7,8,9,10,11,12,13].

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