Integrative analysis reveals disease-associated genes and biomarkers for prostate cancer progression

Yin Li,Wanwipa Vongsangnak,Bairong Shen,Luonan Chen

doi:10.1186/1755-8794-7-s1-s3

Yin Li, Wanwipa Vongsangnak + Show 2 more

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https://doi.org/10.1186/1755-8794-7-s1-s3

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Abstract

BackgroundProstate cancer is one of the most common complex diseases with high leading cause of death in men. Identifications of prostate cancer associated genes and biomarkers are thus essential as they can gain insights into the mechanisms underlying disease progression and advancing for early diagnosis and developing effective therapies.MethodsIn this study, we presented an integrative analysis of gene expression profiling and protein interaction network at a systematic level to reveal candidate disease-associated genes and biomarkers for prostate cancer progression. At first, we reconstructed the human prostate cancer protein-protein interaction network (HPC-PPIN) and the network was then integrated with the prostate cancer gene expression data to identify modules related to different phases in prostate cancer. At last, the candidate module biomarkers were validated by its predictive ability of prostate cancer progression.ResultsDifferent phases-specific modules were identified for prostate cancer. Among these modules, transcription Androgen Receptor (AR) nuclear signaling and Epidermal Growth Factor Receptor (EGFR) signalling pathway were shown to be the pathway targets for prostate cancer progression. The identified candidate disease-associated genes showed better predictive ability of prostate cancer progression than those of published biomarkers. In context of functional enrichment analysis, interestingly candidate disease-associated genes were enriched in the nucleus and different functions were encoded for potential transcription factors, for examples key players as AR, Myc, ESR1 and hidden player as Sp1 which was considered as a potential novel biomarker for prostate cancer.ConclusionsThe successful results on prostate cancer samples demonstrated that the integrative analysis is powerful and useful approach to detect candidate disease-associate genes and modules which can be used as the potential biomarkers for prostate cancer progression. The data, tools and supplementary files for this integrative analysis are deposited at http://www.ibio-cn.org/HPC-PPIN/.

Highlights

Prostate cancer is one of the most common complex diseases with high leading cause of death in men
In summary, we proposed an integrative analysis based on the gene expression profiles and the reconstructed protein-protein interaction network for prostate cancer, in contrast to the conventional methods of examining differential genes expression or proteins expression
The achieved significant modules resulted in the identification of the candidate disease-associated genes which were regarded as potential module biomarker

Summary

Introduction

Prostate cancer is one of the most common complex diseases with high leading cause of death in men. In the post-genomics era, there is an explosion of biological data and information generated from highthroughput technologies which have rapidly provided an unprecedented multi-level omics data [7]. Such transcriptomics, referred to as gene expression profiling can comprehensively survey the entire human genomics. Chuang et al showed that it could be useful to extract co-expressed functional sub-networks for metastasis of breast cancer through integrating transcriptomics data with proteinprotein interaction to obtain higher classification accuracy [19]. There were similar studies for analysis of sub-networks and/or hub proteins which had been helpful for the understanding of the metastasis of cancer at the molecular level [18]

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