Q-omics provides the consensus-scored DLAT profile across patient tissues and cancer cell-line models. DLAT expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, DLAT is differentially expressed in 9, with the highest sampling consensus in LIHC. Additionally, DLAT protein abundance shows 27,455 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, LIHC, and GBM as cancer lineages where DLAT 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 DLAT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DLAT survival associations across molecular data types. DLAT RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DLAT RNA expression–survival associations across cancer types. High DLAT expression shows unfavorable associations in LIHC, UVM and ACC, but favorable associations in KIRC, COAD and READ. 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 DLAT RNA expression.
This table summarizes DLAT 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 THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for DLAT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DLAT shows lower tumor expression in THCA, KIRC and KIRP and higher tumor expression in LIHC, LUAD and STAD. The LIHC box plot shows higher DLAT RNA expression in tumor versus normal tissue (log2 FC = +1.549, t-test p < 0.001).
This table shows molecular features associated with DLAT in patient tissues and cancer cell lines. In patient samples, DLAT 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, DLAT 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 BLOOD_Lymphoma and BLOOD_Myeloma.
