Random forest and partial least squares have proved wide applicability in numerous contexts. However, the combination of these versatile tools has seldom been studied. Inspired by a relatively new decision tree ensemble called rotation forest, we introduce a new survival ensemble algorithm using partial least squares regression and the Buckley-James estimator within the framework of random forest. First, the approach taken to cope with the high dimensionality is to reduce the dimension by a random subspace method. Then, censored survival times are imputed by the Buckley-James estimator. After dimension reduction and time imputation, partial least squares regression is applied to extract the features. Similar to rotation forest, all extracted components are used as covariates in a bagged survival tree to predict the survival probabilities. Experimental results on a variety of simulation and real datasets demonstrate that the proposed approach is a strong competitor to other popular survival prediction models under high or ultra-high dimensional setting.