This article is part of the supplement: IEEE 7th International Conference on Bioinformatics and Bioengineering at Harvard Medical School

Research

Transcription factor and microRNA regulation in androgen-dependent and -independent prostate cancer cells

Guohua Wang^1,2,3 , Yadong Wang^1,3 , Weixing Feng^2,3,6 , Xin Wang^2,3,6 , Jack Y Yang³ , Yuming Zhao^1,2,3 , Yue Wang⁵ and Yunlong Liu^2,3,4

¹School of Computer Science and Technology, Harbin Institute of Technology, Harbin, Heilongjiang 150001, PR China

²Division of Biostatistics Department of Medicine, Indiana University School of Medicine, Indianapolis, IN 46202, USA

³Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, IN 46202, USA

⁴Center for Medical Genomics, Indiana University School of Medicine, Indianapolis, IN 46202, USA

⁵Department of Surgery, Indiana University School of Medicine, Indianapolis, IN 46202, USA

⁶College of Automation, Harbin Engineering University, Harbin, Heilongjiang 150001, PR China

author email corresponding author email

BMC Genomics 2008, 9(Suppl 2):S22doi:10.1186/1471-2164-9-S2-S22

Published:	16 September 2008

Abstract

Background

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. 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.

Results

From 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.

Conclusion

We 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.