Q-omics provides the consensus-scored DTNA profile across patient tissues and cancer cell-line models. DTNA expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, DTNA is differentially expressed in 15, with the highest sampling consensus in BLCA. Additionally, DTNA protein abundance shows 24,465 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight BLCA, and LUAD as cancer lineages where DTNA 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 DTNA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DTNA survival associations across molecular data types. DTNA RNA expression shows survival associations in the most cancer types (22), followed by mutation status (8) 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 DTNA RNA expression–survival associations across cancer types. High DTNA expression shows unfavorable associations in BLCA, UCEC and LGG, but favorable associations in KIRP, PAAD and KIRC. The BLCA 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 BLCA as the clearest survival context for DTNA RNA expression.
This table summarizes DTNA 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 7. The strongest signals are observed in BLCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for DTNA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DTNA shows lower tumor expression in BLCA, UCEC and LUSC and higher tumor expression in LIHC, KIRP and KICH. The BLCA box plot shows higher DTNA RNA expression in normal versus tumor tissue (log2 FC = −4.489, t-test p < 0.001).
This table shows molecular features associated with DTNA in patient tissues and cancer cell lines. In patient samples, DTNA shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, DTNA 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 LUNG_NSCLC_LUAD and SOFT_TISSUE.
