Sparse Gene Coexpression Network Analysis Reveals EIF3J-AS1 as a Prognostic Marker for Breast Cancer

Complexity, Jun 2018

Predictive and prognostic biomarkers facilitate the selection of treatment strategies that can improve the survival of patients. Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in cancer progression, with diagnostic and prognostic potential. However, few prognostic lncRNAs are reported for breast cancer, and little is known about their functions that contribute to cancer pathogenesis. In this paper, we used weighted correlation network analysis (WGCNA) to construct networks containing noncoding and protein-coding genes based on their expression in 1097 breast cancer patients. The differentially expressed genes were significantly overlapped with gene modules regulating cell cycle and cell adhesion. The cell cycle-related lncRNAs were consistently downregulated in breast cancer. One lncRNA, EIF3J-AS1, is significantly associated with clinicopathological characteristics, including tumor size, lymph node metastasis, estrogen receptor (ER), and progesterone receptor (PR) status. Kaplan–Meier survival analysis revealed that EIF3J-AS1, a downregulated lncRNA in breast tumor, is a potential prognostic marker for breast cancer. EIF3J-AS1 may function in an estrogen-independent manner and could be inhibited by the compound FDI-6. Therefore, integrating sparse gene coexpression network and clinicopathological features can accelerate identification and functional characterization of novel prognostic lncRNAs in breast cancer.

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Sparse Gene Coexpression Network Analysis Reveals EIF3J-AS1 as a Prognostic Marker for Breast Cancer

Sparse Gene Coexpression Network Analysis Reveals EIF3J-AS1 as a Prognostic Marker for Breast Cancer Xin Chen, Zuyuan Yang, Chao Yang, Kan Xie, Weijun Sun, and Shengli Xie Guangdong Key Laboratory of IoT Information Technology, School of Automation, Guangdong University of Technology, Guangzhou, Guangdong, China Correspondence should be addressed to Shengli Xie; nc.ude.tudg@eixlhs Received 5 October 2017; Revised 13 May 2018; Accepted 23 May 2018; Published 12 June 2018 Academic Editor: Vittorio Loreto Copyright © 2018 Xin Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract Predictive and prognostic biomarkers facilitate the selection of treatment strategies that can improve the survival of patients. Accumulating evidence indicates that long noncoding RNAs (lncRNAs) play important roles in cancer progression, with diagnostic and prognostic potential. However, few prognostic lncRNAs are reported for breast cancer, and little is known about their functions that contribute to cancer pathogenesis. In this paper, we used weighted correlation network analysis (WGCNA) to construct networks containing noncoding and protein-coding genes based on their expression in 1097 breast cancer patients. The differentially expressed genes were significantly overlapped with gene modules regulating cell cycle and cell adhesion. The cell cycle-related lncRNAs were consistently downregulated in breast cancer. One lncRNA, EIF3J-AS1, is significantly associated with clinicopathological characteristics, including tumor size, lymph node metastasis, estrogen receptor (ER), and progesterone receptor (PR) status. Kaplan–Meier survival analysis revealed that EIF3J-AS1, a downregulated lncRNA in breast tumor, is a potential prognostic marker for breast cancer. EIF3J-AS1 may function in an estrogen-independent manner and could be inhibited by the compound FDI-6. Therefore, integrating sparse gene coexpression network and clinicopathological features can accelerate identification and functional characterization of novel prognostic lncRNAs in breast cancer. 1. Introduction Breast cancer is a highly heterogeneous disease, which is commonly divided into five subtypes, basal-like, HER2, luminal A, luminal B, and normal-like, using histopathological status of either estrogen receptor (ER), human epidermal growth factor receptor (HER2), or a gene expression-based classifier (PAM50) [1]. The use of the mRNA-based prognostic marker, comprised of multiple differentially expressed genes, has been supported by clinical guidelines, which assists the clinical treatment of breast cancer by integrating clinicopathological factors [2, 3]. Gene coexpression networks (GCNs) have been widely used in the studies of cancer for the identification of prognostic signature [4]. GCN from transcriptomic profiles facilitates elucidating gene interactions and exploring regulatory mechanisms [5]. For each gene expression profile, it contains expressions of tenths of thousands of genes in detected samples. The coexpression network is constructed based on the pairwise gene correlation matrix. In the network, each node represents one gene, while each edge represents a pair of genes with highly correlated expression pattern. The large coexpression network is not easy to interpret because of its high dimensionality. Besides, there are few master regulatory genes which basically control the state of the network [6]. It is promising to decompose the sparse network into smaller components [7, 8], which are also referred to as gene modules. GCN is quite sparse with only a few “hub” genes densely connected to each other. For years, the scale-free network model has been supported for biological networks [9]. For example, sparse signal transduction networks follow the scale-free properties. In E. coli and S. cerevisiae, the degree distribution is [10], which implies that majority of the molecules are involved in few interactions and minority of them have many interactions [9, 11]. Long noncoding RNA (lncRNA), with length longer than 200 nt, has been regarded as the dark matter of the genome for decades. However, with the development and application of next-generation sequencing (NGS), lncRNAs have been found to have a myriad of molecular functions in diseases including cancers [12]. LncRNAs such as HOTAIR and MALAT1 had been reported as a prognostic marker for breast cancer [13, 14]. Differential analysis and coexpression network has been successfully applied to identify prognostic lncRNAs in breast cancer [15]. Therefore, in this study, weighted correlation network analysis (WGCNA) was used to identify modules of highly correlated genes. Then, we focus on those modules significantly enriched by differentially expressed genes, which play important roles in breast cancer. The deregulated lncR (...truncated)


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Xin Chen, Zuyuan Yang, Chao Yang, Kan Xie, Weijun Sun, Shengli Xie. Sparse Gene Coexpression Network Analysis Reveals EIF3J-AS1 as a Prognostic Marker for Breast Cancer, Complexity, 2018, 2018, DOI: 10.1155/2018/1656273