Q-omics provides the consensus-scored CEP63 profile across patient tissues and cancer cell-line models. CEP63 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, CEP63 is differentially expressed in 10, with the highest sampling consensus in LIHC. Additionally, CEP63 RNA expression shows 20,922 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UCS, LIHC, and ACC as cancer lineages where CEP63 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 CEP63 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CEP63 survival associations across molecular data types. CEP63 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (6) 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 CEP63 RNA expression–survival associations across cancer types. High CEP63 expression shows unfavorable associations in ACC, LIHC, KICH and LUSC, but favorable associations in UCS and SKCM. The UCS Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .004). Together, the overview and detailed table identify UCS as the clearest survival context for CEP63 RNA expression.
This table summarizes CEP63 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 3. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for CEP63. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CEP63 shows lower tumor expression in THCA and UCEC and higher tumor expression in LIHC, HNSC, CHOL and KIRC. The LIHC box plot shows higher CEP63 RNA expression in tumor versus normal tissue (log2 FC = +0.682, t-test p < 0.001).
This table shows molecular features associated with CEP63 in patient tissues and cancer cell lines. In patient samples, CEP63 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, CEP63 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and BLOOD_Leukemia.
