Abstract
BackgroundA recent paper has revealed a novel cell death pathway, cuproptosis, a programmed cell death based on copper. This study aimed to evaluate the pan-cancer genomics and clinical association of cuproptosis and copper metabolism-related cell death genes, including SLC25A3, SLC25A37, SLC31A1, FDX1, DLAT, LIAS, ATP7A, ATP7B, COX17, SCO1, SCO2, COX11, and COX19.MethodsBy mining multi-omics profiling data, we performed a comprehensive and systematic characterization of cuproptosis genes across more than 9,000 samples of over 30 types of cancer.ResultsATP7B and ATP7A were the two most frequently mutated copper cell death genes in cancer. UCEC and SKCM were the two cancer types that have the highest mutation rates while the mutation of LIAS was associated with worse survival of BRCA. Brain cancer was potentially affected by copper cell death because of the difference in copper cell death gene expression among subtypes and stages. On the contrary, KIRC might have a lower cuproptosis activity because of the decrease in copper cell death gene expression. In lung cancer and kidney cancer, most of the cancer–noncancer expression patterns of copper cell death genes were consistent between mRNA and protein levels. Some of the cuproptosis gene expression was associated with the survival of LGG, KIRC, and ACC. The top five expression-copy numbers correlating cancer types were BRCA, OV, LUSC, HNSC, BLCA, and LUAD. Generally, the copy number variations of these genes in KIRC, UCEC, and LGG were associated with survival. The expression of DLAT, LIAS, and ATP7B was negatively correlated with the methylation in most of the cancer types. The copper cell death genes regulating miRNA and pathway regulation networks were constructed. The copper cell death genes were correlated with immune cell infiltration levels of multiple immune cells. These genes were correlated with the sensitivity of cancer cells to multiple drugs.ConclusionCopper cell death genes are potentially involved in many cancer types and can be developed as candidates for cancer diagnosis, prognosis, and therapeutic biomarkers.