dedicator of cytokinesis 9Genealiases: ZIZ1 · ZIZIMIN1
Q-omics provides the consensus-scored DOCK9 profile across patient tissues and cancer cell-line models. DOCK9 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, DOCK9 is differentially expressed in 11, with the highest sampling consensus in THCA. Additionally, DOCK9 protein abundance shows 24,755 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, THCA, and GBM as cancer lineages where DOCK9 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 DOCK9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DOCK9 survival associations across molecular data types. DOCK9 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (10) 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 DOCK9 RNA expression–survival associations across cancer types. High DOCK9 expression shows unfavorable associations in PAAD, ACC, UVM and BLCA, but favorable associations in KIRC and BRCA. 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 DOCK9 RNA expression.
This table summarizes DOCK9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 7. The strongest signals are observed in THCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for DOCK9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DOCK9 shows lower tumor expression in LUAD, LUSC, KICH and BRCA and higher tumor expression in THCA and LIHC. The THCA box plot shows higher DOCK9 RNA expression in tumor versus normal tissue (log2 FC = +1.075, t-test p < 0.001).
This table shows molecular features associated with DOCK9 in patient tissues and cancer cell lines. In patient samples, DOCK9 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, DOCK9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
