Statistical Methods Applied to Gene Expression Data to Explore Cancer Features

A small number of epithelial ovarian cancer cases are deemed preventable and its overall survival rates are low.The developments in omics data analysis paves a way for biomarker discovery for epithelial ovarian cancer in order to improve survival rates and prevent its development.This report provides analysis of gene expression data for epithelial ovarian cancer to compare the gene expression of 99 epithelial ovarian cancer samples and 4 non-cancerous ovary samples.Serous and endometrioid epithelial ovarian cancer subtypes were most similar based on hierarchical clustering.Serous was the subtype with the most differentially expressed genes when compared with normal ovary samples whereas mucinous had the least by the Wilcoxon Rank-Sum test (Benjamini Hochberg, p < 0.05).The number of down-regulated genes exceeded the number of up-regulated genes when comparing each cancer subtype with normal ovary samples.In this case, the clear cell subtype had the greatest number of dysregulated genes when compared to normal ovaries whereas endometrioid had the least.The dysregulated genes were found by fold change analysis (F C > 2 or F C < 0.5).Differences in gene expression levels between epithelial ovarian cancer subtypes were suggested due to 11, 181 differentially expressed genes identified when comparing expression levels in all sample groups by the Kruskal-Wallis test (Benjamini Hochberg, p < 0.05).Genes proposed as biomarkers for (1) epithelial ovarian cancer and (2) individual epithelial ovarian cancer subtypes, when compared with normal ovaries included MUC1, SCNN1A, CD24, ITM2A, AGR2 and WFDC2.