Modern gene expression technologies enable simultaneous measurement of thousands of genes, thus are important for predicting patient survival. However, survival analysis with gene expression data is challenging due to the high dimensionality. Proper identification of survival relevant genes is imperative for building suitable prediction models. Gene expressions are typically subject to measurement errors introduced from the complex experimental procedure and the measurement error is often ignored. In this talk, the effect of measurement error on the identification of survival relevant genes is explored under the accelerated failure time model. Survival relevant genes are identified by regularizing the weighted least square estimator with the adaptive LASSO penalty. The simulation-extrapolation method is applied to adjust for the impact of measurement error. The performance of the proposed method is assessed by simulation studies and illustrated by a real study.