Abstract

BackgroundProstate cancer is one of the leading causes of cancer death in men. Androgen ablation, the most commonly-used therapy for progressive prostate cancer, is ineffective once the cancer cells become androgen-independent. The regulatory mechanisms that cause this transition (from androgen-dependent to androgen-independent) remain unknown. In this study, based on the microarray data comparing global gene expression patterns in the prostate tissue between androgen-dependent and -independent prostate cancer patients, we indentify a set of transcription factors and microRNAs that potentially cause such difference, using a model-based computational approach.ResultsFrom 335 position weight matrices in the TRANSFAC database and 564 microRNAs in the microRNA registry, our model identify 5 transcription factors and 7 microRNAs to be potentially responsible for the level of androgen dependency. Of these transcription factors and microRNAs, the estimated function of all the 5 transcription factors are predicted to be inhibiting transcription in androgen-independent samples comparing with the dependent ones. Six out of 7 microRNAs, however, demonstrated stimulatory effects. We also find that the expression levels of three predicted transcription factors, including AP-1, STAT3 (signal transducers and activators of transcription 3), and DBP (albumin D-box) are significantly different between androgen-dependent and -independent patients. In addition, microRNA microarray data from other studies confirm that several predicted microRNAs, including miR-21, miR-135a, and miR-135b, demonstrate differential expression in prostate cancer cells, comparing with normal tissues.ConclusionWe present a model-based computational approach to identify transcription factors and microRNAs influencing the progression of androgen-dependent prostate cancer to androgen-independent prostate cancer. This result suggests that the capability of transcription factors to initiate transcription and microRNAs to facilitate mRNA degradation are both decreased in androgen-independent prostate cancer. The proposed model-based approach indicates that considering combinatorial effects of transcription factors and microRNAs in a unified model provides additional transcriptional and post-transcriptional regulatory mechanisms on global gene expression in the prostate cancer with different hormone-dependency.

Highlights

  • Prostate cancer is one of the leading causes of cancer death in men

  • We present a model-based computational approach to identify transcription factors and microRNAs influencing the progression of androgen-dependent prostate cancer to androgenindependent prostate cancer

  • The proposed model-based approach indicates that considering combinatorial effects of transcription factors and microRNAs in a unified model provides additional transcriptional and post-transcriptional regulatory mechanisms on global gene expression in the prostate cancer with different hormone-dependency

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Summary

Introduction

Prostate cancer is one of the leading causes of cancer death in men. Androgen ablation, the most commonly-used therapy for progressive prostate cancer, is ineffective once the cancer cells become androgen-independent. Several transcription factors, including AP-1 [7], NFκB[10], and EGR (early growth response factor) [9] etc, were reported to be related to prostate cancer progression From these experiments, important molecular mechanisms were identified to contribute to the cancer development, including androgen amplification, promiscuous binding, outlaw pathway, bypass pathway, and androgen receptor coregulators [3,4]. Important molecular mechanisms were identified to contribute to the cancer development, including androgen amplification, promiscuous binding, outlaw pathway, bypass pathway, and androgen receptor coregulators [3,4] Despite these discoveries, the complete mechanism of hormone dependency in prostate cancer regulation remains unclear. Such mechanism is further complicated with the recent discovery of microRNA, a class of non-coding RNAs that regulate gene expression in the post-transcriptional level

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