Cancer Stemness Online: A Resource for Investigating Cancer Stemness and Associations with Immune Response.

Abstract

Cancer progression involves the gradual loss of a differentiated phenotype and the acquisition of progenitor and stem-cell-like features, which are potential culprits of immunotherapy resistance. Although the state-of-art predictive computational methods have facilitated the prediction of cancer stemness, currently there is no efficient resource that can meet various usage requirements. Here, we present the Cancer Stemness Online, an integrated resource for efficiently scoring cancer stemness potential at the bulk and single-cell levels. The resource integrates 8 robust predictive algorithms as well as 27 signature gene sets associated with cancer stemness for predicting stemness scores. Downstream analyses were performed from five different aspects, including identifying the signature genes of cancer stemness, exploring the associations with cancer hallmarks, cellular states, the immune response, and communication with immune cells; investigating the contributions to patient survival; and performing a robustness analysis of cancer stemness among different methods. Moreover, the pre-calculated cancer stemness atlas for more than 40 cancer types can be accessed by users. Both the tables and diverse visualizations of the analytical results are available for download. Together, Cancer Stemness Online is a powerful resource for scoring cancer stemness and expanding the downstream functional interpretation, including immune response as well as cancer hallmarks. Cancer Stemness Online is freely accessible at http://bio-bigdata.hrbmu.edu.cn/CancerStemnessOnline.