GENE EXPRESSION DATA ANALYSIS IN SUBTYPES OF OVARIAN CANCER USING COVARIANCE ANALYSIS

Abstract

Many studies have used microarray technology to identify the molecular signatures of human cancer, yet the critical features of these often unmanageably large set of signatures remain elusive. We have investigated co-expression pattern in four subtypes of ovarian cancer from 104 cancer patients using covariance analysis, treating each subtype of ovarian cancer as a distinct disease entity. We sought gene pairs that were transcriptionally co-expressed in one or multiple subtypes of ovarian cancer, establishing a high confidence network of 87 genes interconnected by significantly high co-expression links that were observed in at least two subtypes of ovarian cancer. We have shown that certain groups of co-expressed gene pairs are cancer subtype specific, through demonstrating significant differences in co-expression patterns of gene pairs between subtypes of ovarian cancer. In addition, we identified a set of 24 genes that classified patients into specific cancer subtypes with a misclassification error rate of less than 5%. Our findings illustrate how large public microarray gene expression datasets could be exploited for identification of cancer subtype specific molecular signatures, and how to classify cancer patients into specific subtypes of cancer using gene expression profiles.