Using Data from the Cancer Genome Atlas to Investigate Molecular Events Related to Ovarian Cancer

Ovarian Cancer is the most lethal gynecologic malignancy. Analyzing the molecular events related to ovarian cancer helps understand the pathogenesis of ovarian cancer from a genetic point of view. As miRNA singletons have been found significantly related to ovarian cancer and many other cancers, miRNAs have been recognized as an important riboregulator of gene expression. However, little is known about how pairs of miRNA expression profiles associate with ovarian cancer. In our analysis, we explored the combinatorial effects of miRNA pairs on regulating gene expression. We assessed the non-additive interaction between miRNA pairs on gene expression of patients that carry high grade ovarian cancer. We demonstrate how different miRNAs collectively contribute to ovarian cancer. We will illustrate two examples of miRNA pairs, hsa.miR. 937 & hsa.let. 7b and hsa.miR. 1277& hsa.miR. 485.3b, that we found exhibit non-additive interaction pattern on affecting gene expression of the patients with high grade Ovarian Cancer. Introduction: Ovarian cancer is the second most common gynecologic cancer in the United States and sixth most common cancer in women worldwide[1,2,3]. In 2010 alone, there were 13,850 deaths from of ovarian cancer with an estimated 21,888 new cases diagnosed [4]. Although 90% of the patients with early-stage ovarian cancer could successfully survive 5 years or longer after initial diagnosis, only 21% of the patients who were diagnosed at advanced-stage could achieve that [2]. Due to the lack of robust methods for early detection, 19% of all ovarian cancer is diagnosed at early stage [5]. Thus, more understanding on the pathogenesis of ovarian cancer is needed for helping to improve the early stage detection diagnosis. Analyzing the molecular events related to ovarian cancer give the best help on understanding the pathogenesis of ovarian cancer from a genetic point of view. In order to identify the genetic alteration associates with the malignant phenotype of ovarian, many researchers start investigation on analyzing how miRNA expression profiles contribute to ovarian cancer. miRNA is a small and new class of noncoding RNAs that plays an important role in cellcycle progression, tissue differentiation and organ development [5,7]. Many miRNA singletons have been found significantly related to many other cancers, such as breast cancer, pancreatic cancer and lung cancer. [7,8,9] Several recent studies have also successfully identified that particular miRNA singletons such as miR-21, miR-125a, miR125b[3], miR-200a, miR-141, miR-199a [5], are significantly differentially or over expressed in the miRNA expression profile of ovarian cancer patients. Although miRNA has been recognized as a riboregulator of gene expression [3] and many miRNA singletons have been discovered as essential cancer bio-makers, little is known about how pairs of miRNA expression profiles associate with ovarian cancer. In this report, we represent the result of genomewide miRNA expression profiles in a large set of high grade Ovarian Serous Cystadenocarcinoma (OSC) tissue, a type of highly aggressive epithelial ovarian tumor that accounts for 90% of all ovarian cancer [6]. We assessed the non-additive interaction between miRNA pairs on gene expression of patients that carry OSC. We demonstrate how different miRNAs collectively contributes to this high aggressive OSC. Material and Methods: An overview of the methods used can be found in Research Process Chart in Figure1. Data from The Cancer Genome Atlas (TCGA): We acquired the OSC miRNA expression data, gene expression data and clinical data from TCGA data portal. (Available at: http://tcga-data.nci.nih. gov/tcga/) The level three OSC data from TCGA were analyzed for both miRNA expression and gene expression. In TCGA, level three data represents the data which are background corrected, molecular abnormalities interpreted and normalized. miRNA Expression Data: The miRNA microarray platform of miRNA expression data is Agilent 8*15K performed by University of North Carolina(UNC). The measurements for 799 miRNAs were included in the miRNA expression data in our analysis. Figure 1: Research Process Chart. Gene Expression Data: The platform of gene expression data is Affymetrix HT Human Genome U133 Array Plate Set performed by Massachusetts Institute of Technology (MIT). We have included 12042 genes in our analysis. We have averaged the expression values of the duplicated samples from the same patients in miRNA and gene expression data. We have also removed patients whose tumor stage information is null. 493 patients who have been annotated having OSC stage II to IV in the clinical data are finally selected in our analysis. (Table 1) Zscore Normalization: Before any methods are applied, both miRNA and gene expression data are pre-processed by zscore normalization: subtracting the mean and dividing by standard deviation for data standardization. Construct Gene Co-expression Network: We performed Weighted Gene Co-expression Network Analysis (WGCNA) [10-13] on OSC gene expression data. The purpose of applying WGCNA is to attempt to identify co-expressed genes and to combine the high correlated genes into gene modules (clusters). Since gene modules might associate with biological pathways [14], combining genes into modules is an effective scheme for reducing the dimension of gene expression data from a level of thousands into a smaller set of biologically meaningful gene clusters. Table1: Data Information *clinical data were last updated on August 10th 2010 In WGCNA, each gene is considered a node in the network. The distance between a pair of gene nodes is first determined by their pair-wise Pearson correlation value. The gene network is constructed based on all pair-wise Pearson correlation values between the gene nodes in the network. The pair-wise Pearson correlation was calculated across all patients for all genes in the gene expression data matrix. In order to emphasize the large Pearson correlation value, a power β ≥ 1, known as soft threshold as against hard threshold in un-weighted network, needs to be chosen to raise the power of the absolute value of Pearson correlation by the formula: adjij = |cor(xi, xj)| ^β This adjij is defined as the adjacency of the genes in an unsigned weighted gene co-expression network. Picking a proper value for the soft threshold β is an essential step in WGCNA. In order to allow the network to keep the continuous nature of the gene co-expression information, β should be chosen obeying the criterion of scale-free topology. [10] In this way, a weighted network exhibits its advantages on the robustness in hierarchical clustering analysis [10,14]. As we applied WGCNA to gene expression data in our analysis, we chose soft power threshold β = 4, which is the smallest value reaching the level of 0.9 on the scale independence. (Figure 2) Type Platform No. of Patients No. of Singletons miRNAExpressions UNC__HmiRNA_8x15Kv2 491 799 GeneExpressions BI__HT_HGU133A 491 12042