Q-omics provides the consensus-scored CERK profile across patient tissues and cancer cell-line models. CERK expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, CERK is differentially expressed in 15, with the highest sampling consensus in COAD. Additionally, CERK RNA expression shows 19,484 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight HNSC, COAD, and ACC as cancer lineages where CERK 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 CERK — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CERK survival associations across molecular data types. CERK RNA expression shows survival associations in the most cancer types (26), followed by mutation status (3) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CERK RNA expression–survival associations across cancer types. High CERK expression shows unfavorable associations in ACC, BLCA and OV, but favorable associations in HNSC, SCLC and PAAD. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify HNSC as the clearest survival context for CERK RNA expression.
This table summarizes CERK tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 4. The strongest signals are observed in COAD for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for CERK. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CERK shows lower tumor expression in COAD, KICH, KIRP, THCA and READ and higher tumor expression in LIHC. The COAD box plot shows higher CERK RNA expression in normal versus tumor tissue (log2 FC = −0.903, t-test p < 0.001).
This table shows molecular features associated with CERK in patient tissues and cancer cell lines. In patient samples, CERK 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, CERK RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
