Identification of upstream regulators for prognostic expression signature genes in colorectal cancer

BMC Systems Biology

Sunghoon Kim¹  Wankyu Kim²  Katsuhisa Horimoto⁶  Jin Woo Choi³  Kyoohyoung Rho⁴  Taejeong Bae⁵ 

[1] World Class University Program Department of Molecular Medicine and Biopharmaceutical Sciences, Seoul National University, Seoul 151-742, South Korea;Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, 52 Ewhayeodae-gil, Seodaemun-gu, Seoul 120-750, South Korea;Department of Pharmacology and Wonkwang Institute of Dental Research, School of Dentistry, Wonkwang University, Iksan, Chonbuk 570-749, South Korea;DNA Link Inc, Seoul, South Korea;Medicinal Bioconvergence Research Center, Advanced Institutes of Convergence Technology, Suwon 443-270, South Korea;Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, Tokyo 135-0064, Japan

关键词: Network inference;    Transcriptional network;    Colorectal cancer;    Gene signature;   

Others  :  1142338
DOI  :  10.1186/1752-0509-7-86

 received in 2013-04-23, accepted in 2013-09-02,  发布年份 2013

【 摘 要 】

Background

Gene expression signatures have been commonly used as diagnostic and prognostic markers for cancer subtyping. However, expression signatures frequently include many passengers, which are not directly related to cancer progression. Their upstream regulators such as transcription factors (TFs) may take a more critical role as drivers or master regulators to provide better clues on the underlying regulatory mechanisms and therapeutic applications.

Results

In order to identify prognostic master regulators, we took the known 85 prognostic signature genes for colorectal cancer and inferred their upstream TFs. To this end, a global transcriptional regulatory network was constructed with total >200,000 TF-target links using the ARACNE algorithm. We selected the top 10 TFs as candidate master regulators to show the highest coverage of the signature genes among the total 846 TF-target sub-networks or regulons. The selected TFs showed a comparable or slightly better prognostic performance than the original 85 signature genes in spite of greatly reduced number of marker genes from 85 to 10. Notably, these TFs were selected solely from inferred regulatory links using gene expression profiles and included many TFs regulating tumorigenic processes such as proliferation, metastasis, and differentiation.

Conclusions

Our network approach leads to the identification of the upstream transcription factors for prognostic signature genes to provide leads to their regulatory mechanisms. We demonstrate that our approach could identify upstream biomarkers for a given set of signature genes with markedly smaller size and comparable performances. The utility of our method may be expandable to other types of signatures such as diagnosis and drug response.

【 授权许可】

   
2013 Bae et al.; licensee BioMed Central Ltd.

【 图 表 】

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