centromere protein QGenealiases: C6orf139 · CENP-Q
Q-omics provides the consensus-scored CENPQ profile across patient tissues and cancer cell-line models. CENPQ expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, CENPQ is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, CENPQ RNA expression shows 19,645 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where CENPQ 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 CENPQ — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CENPQ survival associations across molecular data types. CENPQ RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CENPQ RNA expression–survival associations across cancer types. High CENPQ expression shows unfavorable associations in ACC, LIHC, MESO and KICH, but favorable associations in KIRC and UCS. 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 CENPQ RNA expression.
This table summarizes CENPQ 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 3. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for CENPQ. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CENPQ shows lower tumor expression in THCA and higher tumor expression in HNSC, BLCA, LIHC, STAD and COAD. The HNSC box plot shows higher CENPQ RNA expression in tumor versus normal tissue (log2 FC = +1.267, t-test p < 0.001).
This table shows molecular features associated with CENPQ in patient tissues and cancer cell lines. In patient samples, CENPQ 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, CENPQ RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LUNG_NSCLC_LUAD.
