DNA polymerase kappaGenealiases: DINB1 · DINP · POLQ
Q-omics provides the consensus-scored POLK profile across patient tissues and cancer cell-line models. POLK expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, POLK is differentially expressed in 10, with the highest sampling consensus in THCA. Additionally, POLK RNA expression shows 21,158 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight KIRC, THCA, and THYM as cancer lineages where POLK 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 POLK — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes POLK survival associations across molecular data types. POLK RNA expression shows survival associations in the most cancer types (24), followed by mutation status (7) 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 POLK RNA expression–survival associations across cancer types. High POLK expression shows unfavorable associations in KICH, UVM, CESC, OV and LGG, but favorable associations in KIRC. 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 POLK RNA expression.
This table summarizes POLK 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 THCA for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for POLK. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. POLK shows lower tumor expression in THCA, LUSC and KICH and higher tumor expression in LIHC, HNSC and KIRC. The THCA box plot shows higher POLK RNA expression in normal versus tumor tissue (log2 FC = −0.507, t-test p < 0.001).
This table shows molecular features associated with POLK in patient tissues and cancer cell lines. In patient samples, POLK shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, POLK RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and UPPER_AERODIGESTIVE_TRACT.
