Obtaining biomarkers in cancer progression from outliers of time-series clusters

Studying the expression of transcripts throughout the various stages of prostate cancer may provide insight into the factors that influence the progression of the disease. Moreover, it may also reveal outlier transcripts, which have different trends than the majority of the transcripts. In this study, we use a time-series profile hierarchical clustering method to separate dissimilar groups of aligned transcripts that have maximum distance with the other group expression patterns throughout the various stages/sub-stages of prostate cancer progression. The isolated outliers can serve as biomarkers in analyzing different stages/sub-stages. This paper suggests that the combination of proper clustering, distance function and index validation for clusters are suitable model to find a pattern of trending for transcript abundance throughout different prostate cancer stages/sub-stages. The stages/sub-stages represent the time points, and the growth of the transcript abundance throughout those time points are cubic spline interpolated. The trending throughout those stages can lead to understanding the relationships among the transcripts and provide a better analysis of prostate cancer development through stages.