Q-omics provides the consensus-scored CEP85 profile across patient tissues and cancer cell-line models. CEP85 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, CEP85 is differentially expressed in 16, with the highest sampling consensus in BLCA. Additionally, CEP85 protein abundance shows 32,623 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, BLCA, and GBM as cancer lineages where CEP85 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 CEP85 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CEP85 survival associations across molecular data types. CEP85 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CEP85 RNA expression–survival associations across cancer types. High CEP85 expression shows unfavorable associations in ACC, MESO, LIHC, LGG and KIRP, but favorable associations in KIRC. 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 CEP85 RNA expression.
This table summarizes CEP85 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 7. The strongest signals are observed in BLCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for CEP85. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CEP85 shows lower tumor expression in THCA and higher tumor expression in BLCA, LUAD, HNSC, LUSC and STAD. The BLCA box plot shows higher CEP85 RNA expression in tumor versus normal tissue (log2 FC = +1.630, t-test p < 0.001).
This table shows molecular features associated with CEP85 in patient tissues and cancer cell lines. In patient samples, CEP85 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, CEP85 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in CNS and LUNG_NSCLC_LUAD.
