MicroRNA and Transcription Factor Gene Regulatory Network Analysis Reveals Key Regulatory Elements Associated with Prostate Cancer Progression

• Published in: PloS one 2016-12, Vol.11 (12), p.e0168760
• Main Authors: Sadeghi, Mehdi, Ranjbar, Bijan, Ganjalikhany, Mohamad Reza, M Khan, Faiz, Schmitz, Ulf, Wolkenhauer, Olaf, Gupta, Shailendra K
• Format: Article
• Language: English
• Subjects: Analysis; Apoptosis; Bioinformatics; Biology; Biology and life sciences; Biophysics; Cancer; Computational Biology - methods; Development and progression; Disease Progression; Gene expression; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Gene Regulatory Networks; Genetic aspects; Genetic regulation; Humans; JunB protein; Male; Medicine and Health Sciences; Metastases; Metastasis; MicroRNA; MicroRNAs; MicroRNAs - genetics; miRNA; Network analysis; Physical Sciences; Prostate cancer; Prostatic Neoplasms - genetics; Prostatic Neoplasms - pathology; Regulatory sequences; Research and Analysis Methods; Ribonucleic acid; RNA; Stat3 protein; Studies; Survival analysis; Transcription factors; Transcription Factors - genetics; Transcriptome - genetics; Tumors
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0168760

Technological and methodological advances in multi-omics data generation and integration approaches help elucidate genetic features of complex biological traits and diseases such as prostate cancer. Due to its heterogeneity, the identification of key functional components involved in the regulation and progression of prostate cancer is a methodological challenge. In this study, we identified key regulatory interactions responsible for primary to metastasis transitions in prostate cancer using network inference approaches by integrating patient derived transcriptomic and miRomics data into gene/miRNA/transcription factor regulatory networks. One such network was derived for each of the clinical states of prostate cancer based on differentially expressed and significantly correlated gene, miRNA and TF pairs from the patient data. We identified key elements of each network using a network analysis approach and validated our results using patient survival analysis. We observed that HOXD10, BCL2 and PGR are the most important factors affected in primary prostate samples, whereas, in the metastatic state, STAT3, JUN and JUNB are playing a central role. Benefiting integrative networks our analysis suggests that some of these molecules were targeted by several overexpressed miRNAs which may have a major effect on the dysregulation of these molecules. For example, in the metastatic tumors five miRNAs (miR-671-5p, miR-665, miR-663, miR-512-3p and miR-371-5p) are mainly responsible for the dysregulation of STAT3 and hence can provide an opportunity for early detection of metastasis and development of alternative therapeutic approaches. Our findings deliver new details on key functional components in prostate cancer progression and provide opportunities for the development of alternative therapeutic approaches.