Q-omics provides the consensus-scored DDX5 profile across patient tissues and cancer cell-line models. DDX5 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, DDX5 is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, DDX5 protein abundance shows 27,397 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight BLCA, KICH, and GBM as cancer lineages where DDX5 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 DDX5 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DDX5 survival associations across molecular data types. DDX5 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DDX5 RNA expression–survival associations across cancer types. High DDX5 expression shows unfavorable associations in ACC, LGG and UVM, but favorable associations in BLCA, COAD and KIRC. The BLCA 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 BLCA as the clearest survival context for DDX5 RNA expression.
This table summarizes DDX5 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 7. The strongest signals are observed in KICH for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for DDX5. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DDX5 shows lower tumor expression in KICH, LUSC and THCA and higher tumor expression in LIHC, HNSC and COAD. The KICH box plot shows higher DDX5 RNA expression in normal versus tumor tissue (log2 FC = −1.337, t-test p < 0.001).
This table shows molecular features associated with DDX5 in patient tissues and cancer cell lines. In patient samples, DDX5 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, DDX5 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BLOOD_Leukemia.
