Identifying the Most Appropriate Pattern for Identification of Gene Expression Changes in Ovarian Cancer Using Microarray

Abstract

Background: Microarray technology is an accurate method for recognition of disease association gene alterations. However, there still is not an effective approach for the evaluation of gene expression in ovarian cancer. Objectives: A reliable approach is described to identify genes associated with ovarian cancer. Methods: Microarray gene expression data analysis was applied to correct systematic differences through four different normalization methods; LOESS, 3D LOESS, and neural network (NN3, NN4). Then, three different clustering methods of K-means, fuzzy C-means, and hierarchical methods were examined on corrected gene expression values. The proposed approach was tested on a reliable source of genes’ information, where the entropy of genes in samples and Euclidean distance were used for gene selection. Results: Our findings revealed that a neural-network-based normalization method could better control the effects of non-biological variations from microarray data. Moreover, the hierarchical clustering was more effective compared to other methods, and resulted in the identification of three genes, including BC029410, DUSP2, and ILDR1, as candidates for disease-association genes. Conclusions: According to the finding of the present study, hierarchical clustering with nonlinear-based normalization could have the ability to prioritize genes for ovarian cancer.