Q-omics provides the consensus-scored CCNY profile across patient tissues and cancer cell-line models. CCNY expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, CCNY is differentially expressed in 10, with the highest sampling consensus in LIHC. Additionally, CCNY protein abundance shows 34,679 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, LIHC, and GBM as cancer lineages where CCNY 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 CCNY — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CCNY survival associations across molecular data types. CCNY RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CCNY RNA expression–survival associations across cancer types. High CCNY expression shows unfavorable associations in ACC, LIHC and BLCA, but favorable associations in KIRC, LGG and SCLC. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify ACC as the clearest survival context for CCNY RNA expression.
This table summarizes CCNY tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 10. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for CCNY. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CCNY shows lower tumor expression in KICH, THCA and LUAD and higher tumor expression in LIHC, HNSC and STAD. The LIHC box plot shows higher CCNY RNA expression in tumor versus normal tissue (log2 FC = +0.710, t-test p < 0.001).
This table shows molecular features associated with CCNY in patient tissues and cancer cell lines. In patient samples, CCNY shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, CCNY RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SOFT_TISSUE.
