Effective representation of whole slide images (WSIs) is essential for survival prediction tasks. Previous studies have primarily focused on multimodal approaches, exploring complex fusion techniques to integrate information from different modalities. However, these methods are met with several challenges: (1) Increasingly complex modality fusion techniques result in prolonged model training and inference times. (2) Histology datasets are typically small, making these complex models vulnerable to overfitting. To address these issues and improve a model’s ability to capture effective representations without increasing complexity, we introduce multi-task learning into survival prediction. Specifically, we propose a multi-task survival prediction framework that incorporates tumor staging classification as an auxiliary task, trained simultaneously with the survival prediction task. To the best of our knowledge, this is the first study to integrate tumor staging information into survival prediction. Our method was comprehensively evaluated through unimodal and multimodal experiments across five TCGA datasets. Most experiments demonstrated improved performance, with the best C-Index showing a 16.3% increase.