Q-omics provides the consensus-scored DDX6 profile across patient tissues and cancer cell-line models. DDX6 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, DDX6 is differentially expressed in 11, with the highest sampling consensus in THCA. Additionally, DDX6 protein abundance shows 33,558 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, THCA, and LSCC as cancer lineages where DDX6 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 DDX6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DDX6 survival associations across molecular data types. DDX6 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (6) and mass-spec protein abundance (12). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DDX6 RNA expression–survival associations across cancer types. High DDX6 expression shows unfavorable associations in ACC, but favorable associations in KIRC, SCLC, HNSC, STAD and UCS. 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 DDX6 RNA expression.
This table summarizes DDX6 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 10. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for DDX6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DDX6 shows lower tumor expression in THCA and KICH and higher tumor expression in LIHC, HNSC, CHOL and LUSC. The THCA box plot shows higher DDX6 RNA expression in normal versus tumor tissue (log2 FC = −0.501, t-test p < 0.001).
This table shows molecular features associated with DDX6 in patient tissues and cancer cell lines. In patient samples, DDX6 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, DDX6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BLOOD_Leukemia.
