Classical biomarkers have been used to classify clear cell renal cell carcinoma (ccRCC) patients in a variety of ways, and emerging evidences have indicated that cuproptosis is closely related to mitochondrial metabolism, thereby accelerating the development and progression of ccRCC. Nevertheless, the specific relationship between cuproptosis and the prognosis and treatment of ccRCC remains unclear.
We comprehensively integrated several ccRCC patient datasets into a large cohort. Following that, we systematically analyzed multi-omics data to demonstrate the differences between two cuproptosis clusters.
We identified two cuproptosis clusters in ccRCC patients. Among the two clusters, cluster 1 patients showed favorable prognosis. We then confirmed the significant differences between the two clusters, including more typical cancer hallmarks were enriched in cluster 2 patients; cluster 2 patients were more susceptible to develop mutations and had a lower level of gistic score and mRNAsi. Importantly, both Tumor Immune Dysfunction and Exclusion analysis and subclass mapping algorithm showed that cuproptosis 1 patients were more susceptible to be responded to immunotherapy. In addition, a prognostic signature was successfully developed and also showed prominent predictive power in response to immunotherapy.
As a result of our findings, we were able to classify ccRCC patients according to cuproptosis in a novel way. By constructing the cuproptosis clusters and developing the signature, patients with ccRCC could have a more accurate prognosis prediction and better immunotherapy options.