Q-omics provides the consensus-scored DGKA profile across patient tissues and cancer cell-line models. DGKA expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, DGKA is differentially expressed in 9, with the highest sampling consensus in KIRC. Additionally, DGKA protein abundance shows 21,037 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRP, KIRC, and GBM as cancer lineages where DGKA 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 DGKA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DGKA survival associations across molecular data types. DGKA RNA expression shows survival associations in the most cancer types (23), followed by mutation status (3) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DGKA RNA expression–survival associations across cancer types. High DGKA expression shows unfavorable associations in KIRP, UVM, ACC, HNSC and MESO, but favorable associations in BRCA. The KIRP 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 KIRP as the clearest survival context for DGKA RNA expression.
This table summarizes DGKA 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 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for DGKA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DGKA shows lower tumor expression in COAD and KICH and higher tumor expression in KIRC, LUAD, LUSC and KIRP. The KIRC box plot shows higher DGKA RNA expression in tumor versus normal tissue (log2 FC = +0.785, t-test p < 0.001).
This table shows molecular features associated with DGKA in patient tissues and cancer cell lines. In patient samples, DGKA 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, DGKA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
