The rapid development of computational pathology has brought new opportunities for prognosis prediction using histopathological images. However, the existing deep learning frameworks lack exploration of the relationship between images and other prognostic information, resulting in poor interpretability. Tumor mutation burden (TMB) is a promising biomarker for predicting the survival outcomes of cancer patients, but its measurement is costly. Its heterogeneity may be reflected in histopathological images. Here, we report a two-step framework for prognostic prediction using whole-slide images (WSIs). First, the framework adopts a deep residual network to encode the phenotype of WSIs and classifies patient-level TMB by the deep features after aggregation and dimensionality reduction. Then, the patients' prognosis is stratified by the TMB-related information obtained during the classification model development. Deep learning feature extraction and TMB classification model construction are performed on an in-house dataset of 295 Haematoxylin & Eosin stained WSIs of clear cell renal cell carcinoma (ccRCC). The development and evaluation of prognostic biomarkers are performed on The Cancer Genome Atlas-Kidney ccRCC (TCGA-KIRC) project with 304 WSIs. Our framework achieves good performance for TMB classification with an area under the receiver operating characteristic curve (AUC) of 0.813 on the validation set. Through survival analysis, our proposed prognostic biomarkers can achieve significant stratification of patients' overall survival (P 0.05) and outperform the original TMB signature in risk stratification of patients with advanced disease. The results indicate the feasibility of mining TMB-related information from WSI to achieve stepwise prognosis prediction.