An integrated approach for identifying genes associated with chemotherapy resistance in high-grade serous epithelial ovarian cancer

Abstract

Treatment with chemotherapy is important in limiting the intensity of serous epithelial ovarian cancer. However, not all patients are sensitive to platinum chemotherapy corresponding to longer progression-free survival (PFS >8 months). Koti et al.[1] revealed a set of 204 discriminating genes possessing expression levels, which could influence differential chemotherapy response between the platinum-resistant and platinum-sensitive group of patients. They considered Welch two-sample t-test and non-parametric Mann-Whitney U test to identify the differentially expressed genes. However, both the statistical methods turned out to be unsuitable for microarray data. In this paper, we used three alternative statistical methods to select a combined list of genes and compared the genes that were proposed by Koti et al.[1]. Subsequently, we recommended using sparse principal component analysis (sparse PCA) to identify a final list of genes. Sparse PCA incorporates correlation into account among the genes and helps to draw a biologically important gene discovery. We identified 77 differentially expressed genes, which include 11 new genes that can separate the groups of patients who are platinum-resistant and platinum-sensitive to the chemotherapy. The integrative approach can also be effective in another high dimensional dataset to compare between two groups.