an accurate prostate cancer prognosticator using a seven-gene signature plus gleason score and taking cell type heterogeneity into account准确的前列腺癌预言者使用seven-gene签名+格里森评分和考虑细胞类型的异质性.pdfVIP

An Accurate Prostate Cancer Prognosticator Using a Seven-Gene Signature Plus Gleason Score and Taking Cell Type Heterogeneity into Account 1 2 1,3 1 1 1 Xin Chen , Shizhong Xu , Michael McClelland , Farah Rahmatpanah , Anne Sawyers , Zhenyu Jia *, Dan Mercola1* 1 Department of Pathology and Laboratory Medicine, University of California Irvine, Irvine, California, United States of America, 2 Department of Genetics and Geneticist Botany and Plant Sciences, University of California Riverside, Riverside, California, United States of America, 3 Vaccine Research Institute of San Diego, San Diego, California, United States of America Abstract One of the major challenges in the development of prostate cancer prognostic biomarkers is the cellular heterogeneity in tissue samples. We developed an objective Cluster-Correlation (CC) analysis to identify gene expression changes in various cell types that are associated with progression. In the Cluster step, samples were clustered (unsupervised) based on the expression values of each gene through a mixture model combined with a multiple linear regression model in which cell- type percent data were used for decomposition. In the Correlation step, a Chi-square test was used to select potential prognostic genes. With CC analysis, we identified 324 significantly expressed genes (68 tumor and 256 stroma cell expressed genes) which were strongly associated with the observed biochemical relapse status. Significance Analysis of Microarray (SAM) was then utilized to develop a seven-gene classifier. The Classifier has been validated using two independent Data Sets. The overall prediction accuracy and sensitivity is 71% and 76%, respectively. The incl