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Open Access Research article

Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer

Wei-Chun Chou1, An-Lin Cheng2, Marco Brotto2 and Chun-Yu Chuang1*

Author Affiliations

1 Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu 30013, Taiwan

2 Muscle Biology Research Group-MUBIG, Schools of Nursing & Health Studies, University of Missouri Kansas City, Kansas City, MO 64110, USA

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BMC Genomics 2014, 15:300  doi:10.1186/1471-2164-15-300

Published: 23 April 2014

Abstract

Background

Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments.

Results

ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle.

Conclusions

The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs. This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4th most common of cancer in women.

Keywords:
Endometrial cancer; WGCNA, Network analysis; Hub gene, TCA cycle