copper chaperone for superoxide dismutaseGenealiases: []
Q-omics provides the consensus-scored CCS profile across patient tissues and cancer cell-line models. CCS expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in COAD. Among the 18 cancer types available for tumor–normal comparison, CCS is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, CCS RNA expression shows 18,331 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight COAD, KICH, and THYM as cancer lineages where CCS shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for CCS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CCS survival associations across molecular data types. CCS RNA expression shows survival associations in the most cancer types (20), followed by mutation status (3) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CCS RNA expression–survival associations across cancer types. High CCS expression shows unfavorable associations in COAD, ACC, KICH and PRAD, but favorable associations in KIRC and LGG. The COAD Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify COAD as the clearest survival context for CCS RNA expression.
This table summarizes CCS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for CCS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CCS shows lower tumor expression in KICH and THCA and higher tumor expression in KIRC, HNSC, LUSC and LUAD. The KICH box plot shows higher CCS RNA expression in normal versus tumor tissue (log2 FC = −1.261, t-test p < 0.001).
This table shows molecular features associated with CCS in patient tissues and cancer cell lines. In patient samples, CCS shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, CCS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
