dishevelled binding antagonist of beta catenin 3Genealiases: DAPPER3 · RRR1
Q-omics provides the consensus-scored DACT3 profile across patient tissues and cancer cell-line models. DACT3 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, DACT3 is differentially expressed in 14, with the highest sampling consensus in BLCA. Additionally, DACT3 RNA expression shows 27,719 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRP, BLCA, and GBM as cancer lineages where DACT3 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 DACT3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DACT3 survival associations across molecular data types. DACT3 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) 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 DACT3 RNA expression–survival associations across cancer types. High DACT3 expression shows unfavorable associations in KIRP, ACC, OV, BLCA, LUSC and MESO. 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 DACT3 RNA expression.
This table summarizes DACT3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 5. The strongest signals are observed in BLCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for DACT3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DACT3 shows lower tumor expression in BLCA, KICH, COAD, UCEC and KIRP and higher tumor expression in HNSC. The BLCA box plot shows higher DACT3 RNA expression in normal versus tumor tissue (log2 FC = −4.317, t-test p < 0.001).
This table shows molecular features associated with DACT3 in patient tissues and cancer cell lines. In patient samples, DACT3 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, DACT3 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 BLOOD_Lymphoma and SKIN.
