Q-omics provides the consensus-scored DDX3Y profile across patient tissues and cancer cell-line models. DDX3Y expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, DDX3Y is differentially expressed in 10, with the highest sampling consensus in KIRP. Additionally, DDX3Y RNA expression shows 10,465 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight HNSC, KIRP, and TGCT as cancer lineages where DDX3Y 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 DDX3Y — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DDX3Y survival associations across molecular data types. DDX3Y RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) 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 DDX3Y RNA expression–survival associations across cancer types. High DDX3Y expression shows unfavorable associations in THCA and SCLC, but favorable associations in HNSC, BRCA, UVM and MESO. The HNSC 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 HNSC as the clearest survival context for DDX3Y RNA expression.
This table summarizes DDX3Y tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRP for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for DDX3Y. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DDX3Y shows lower tumor expression in KIRP, UCEC, BLCA, KICH, LUSC and STAD. The KIRP box plot shows higher DDX3Y RNA expression in normal versus tumor tissue (log2 FC = −2.557, t-test p < 0.001).
This table shows molecular features associated with DDX3Y in patient tissues and cancer cell lines. In patient samples, DDX3Y shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, DDX3Y 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 UPPER_AERODIGESTIVE_TRACT and LUNG_SCLC.
