Identification of Biomarkers for Prostate Cancer Prognosis Using a Novel Two-Step Cluster Analysis

Abstract

Abstract. Prognosis of Prostate cancer is challenging due to incomplete assessment by clinical variables such as Gleason score, metastasis stage, surgical margin status, seminal vesicle invasion status and preoperative prostate-specific antigen level. The whole-genome gene expression assay provides us with opportunities to identify molecular indicators for predicting disease outcomes. However, cell composition heterogeneity of the tissue samples usually generates inconsistent results for cancer profile studies. We developed a two-step strategy to identify prognostic biomarkers for prostate cancer by taking into account the variation due to mixed tissue samples. In the first step, an unsupervised EM clustering analysis was applied to each gene to cluster patient samples into subgroups based on the expression values of the gene. In the second step, genes were selected based on χ 2 correlation analysis between the cluster indicators obtained in the first step and the observed clinical outcomes. Two simulation studies showed that the proposed method identified 30% more prognostic genes than the traditional differential expression analysis methods such as SAM and LIMMA. We also analyzed a real prostate cancer expression data set using the new method and the traditional methods. The pathway assay showed that the genes identified with the new method are significantly enriched by prostate cancer relevant pathways such as the wnt signaling pathway and TGF-β signaling pathway. Nevertheless, these genes were not detected by the traditional methods.

An Accurate Prostate Cancer Prognosticator Using a Seven-Gene Signature Plus Gleason Score and Taking Cell Type Heterogeneity into Account

“…The two exclusive features make CC analysis better than traditional gene expression analyses. In a previous study [17] we compared the CC analysis model with traditional gene differential expression analyses such as by SAM and LIMMA. The simulation results showed that the new model outperformed the traditional gene differential expression analyses in terms of sensitivity and specificity.…”

“…The simulation results showed that the new model outperformed the traditional gene differential expression analyses in terms of sensitivity and specificity. In addition, when these methods were applied to prostate cancer data, the CC analysis can identify genes that are significantly enriched or associated with prostate cancer related pathways such as the wnt signaling pathway, ECM-receptor interaction, focal adhesion and TGF- β signaling pathway [17] .…”

“…We developed a novel Cluster-Correlation (CC) analysis procedure [17] for the determination of differential gene expression in various cell types. The CC analysis is implemented in 2 steps, i.e., an unsupervised cluster step and a correlation step ( Figure S1 ).…”

“…Here we investigate whether varying cell type composition plays an important role in the identification of differentially expressed genes. We developed a Cluster-Correlation Analysis model [17] that incorporates a multiple linear regression model to consider cell type composition for samples with known composition. We show that this method may be used to identify differentially expressed genes between biochemical relapse and non-relapse patient samples after prostatectomy.…”