Q-omics provides the consensus-scored DGUOK profile across patient tissues and cancer cell-line models. DGUOK expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, DGUOK is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, DGUOK RNA expression shows 19,011 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where DGUOK 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 DGUOK — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DGUOK survival associations across molecular data types. DGUOK RNA expression shows survival associations in the most cancer types (26), followed by mutation status (2) 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 DGUOK RNA expression–survival associations across cancer types. High DGUOK expression shows unfavorable associations in ACC, KIRP, KIRC, LIHC and ESCA, but favorable associations in BLCA. 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 DGUOK RNA expression.
This table summarizes DGUOK 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 3. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for DGUOK. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DGUOK shows lower tumor expression in KICH and higher tumor expression in HNSC, KIRC, BLCA, LIHC and COAD. The HNSC box plot shows higher DGUOK RNA expression in tumor versus normal tissue (log2 FC = +1.417, t-test p < 0.001).
This table shows molecular features associated with DGUOK in patient tissues and cancer cell lines. In patient samples, DGUOK 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, DGUOK RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in BONE and UPPER_AERODIGESTIVE_TRACT.
