Q-omics provides the consensus-scored DGKQ profile across patient tissues and cancer cell-line models. DGKQ expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in STAD. Among the 18 cancer types available for tumor–normal comparison, DGKQ is differentially expressed in 9, with the highest sampling consensus in HNSC. Additionally, DGKQ protein abundance shows 22,812 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight STAD, HNSC, and GBM as cancer lineages where DGKQ 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 DGKQ — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DGKQ survival associations across molecular data types. DGKQ RNA expression shows survival associations in the most cancer types (22), followed by mutation status (2) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DGKQ RNA expression–survival associations across cancer types. High DGKQ expression shows unfavorable associations in UVM, KIRC, HNSC and THCA, but favorable associations in STAD and SCLC. The STAD 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 STAD as the clearest survival context for DGKQ RNA expression.
This table summarizes DGKQ tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 4. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for DGKQ. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DGKQ shows higher tumor expression in HNSC, LIHC, KIRP, KIRC, STAD and BRCA. The HNSC box plot shows higher DGKQ RNA expression in tumor versus normal tissue (log2 FC = +0.961, t-test p < 0.001).
This table shows molecular features associated with DGKQ in patient tissues and cancer cell lines. In patient samples, DGKQ shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, DGKQ RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and CNS.
