Current survival analysis of cancers confronts two key issues. While comprehensive perspectives provided by data from multiple modalities often promote the performance of survival models, data with inadequate modalities at the testing phase are more ubiquitous in clinical scenarios, which makes multi-modality approaches not applicable. Additionally, incomplete observations (i.e., censored instances) bring a unique challenge for survival analysis, to tackle which, some models have been proposed based on certain strict assumptions or attribute distributions that, however, may limit their applicability. In this paper, we present a mutual-assistance learning paradigm for standalone mono-modality survival analysis of cancers. The mutual assistance implies the cooperation of multiple components and embodies three aspects: 1) it leverages the knowledge of multi-modality data to guide the representation learning of an individual modality via mutual-assistance similarity and geometry constraints; 2) it formulates mutual-assistance regression and ranking functions independent of strong hypotheses to estimate the relative risk, in which a bias vector is introduced to efficiently cope with the censoring problem; 3) it integrates representation learning and survival modeling into a unified mutual-assistance framework for alleviating the requirement of attribute distributions. Extensive experiments on several datasets demonstrate our method can significantly improve the performance of mono-modality survival model.