protein kinase C thetaGenealiases: PRKCT · nPKC-theta
Q-omics provides the consensus-scored PRKCQ profile across patient tissues and cancer cell-line models. PRKCQ expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PRKCQ is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, PRKCQ RNA expression shows 18,603 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight KIRC, and KIRP as cancer lineages where PRKCQ 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 PRKCQ — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PRKCQ survival associations across molecular data types. PRKCQ RNA expression shows survival associations in the most cancer types (20), followed by mutation status (5) 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 PRKCQ RNA expression–survival associations across cancer types. High PRKCQ expression shows unfavorable associations in ACC, but favorable associations in KIRC, SKCM, LIHC, BRCA and ESCA. The KIRC 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 KIRC as the clearest survival context for PRKCQ RNA expression.
This table summarizes PRKCQ tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PRKCQ. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PRKCQ shows lower tumor expression in KIRC, KICH, THCA, LUAD, LUSC and BRCA. The KIRC box plot shows higher PRKCQ RNA expression in normal versus tumor tissue (log2 FC = −1.836, t-test p < 0.001).
This table shows molecular features associated with PRKCQ in patient tissues and cancer cell lines. In patient samples, PRKCQ shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, PRKCQ RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and BLOOD_Leukemia.
