Q-omics provides the consensus-scored DAZ3 profile across patient tissues and cancer cell-line models. DAZ3 expression is associated with patient survival in 5 of 34 cancer types, with the highest sampling consensus in BLCA. Additionally, DAZ3 RNA expression shows 1,233 significant gene co-expression associations, with the highest sampling consensus in KIRC. Together, these results highlight BLCA, and KIRC as cancer lineages where DAZ3 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 DAZ3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DAZ3 survival associations across molecular data types. DAZ3 RNA expression shows survival associations in the most cancer types (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DAZ3 RNA expression–survival associations across cancer types. High DAZ3 expression shows unfavorable associations in BLCA, LIHC, GBM and LGG, but favorable associations in ESCA. 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 DAZ3 RNA expression.
This table shows molecular features associated with DAZ3 in patient tissues and cancer cell lines. In patient samples, DAZ3 shows the broadest associations at the RNA and protein expression levels, with KIRC recurring as the lineage with the largest associated feature set. In cancer cell lines, DAZ3 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 BLOOD_Leukemia and SKIN.
