Q-omics provides the consensus-scored DDX42 profile across patient tissues and cancer cell-line models. DDX42 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, DDX42 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, DDX42 protein abundance shows 27,899 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRP, HNSC, and GBM as cancer lineages where DDX42 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 DDX42 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DDX42 survival associations across molecular data types. DDX42 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (8) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DDX42 RNA expression–survival associations across cancer types. High DDX42 expression shows unfavorable associations in KIRP, ACC, CESC and LIHC, but favorable associations in KIRC and BRCA. 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 DDX42 RNA expression.
This table summarizes DDX42 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 HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for DDX42. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DDX42 shows lower tumor expression in THCA and higher tumor expression in HNSC, LIHC, KIRP, KIRC and CHOL. The HNSC box plot shows higher DDX42 RNA expression in tumor versus normal tissue (log2 FC = +0.615, t-test p < 0.001).
This table shows molecular features associated with DDX42 in patient tissues and cancer cell lines. In patient samples, DDX42 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, DDX42 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_Leukemia and LARGE_INTESTINE.
