Q-omics provides the consensus-scored DDX31 profile across patient tissues and cancer cell-line models. DDX31 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, DDX31 is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, DDX31 RNA expression shows 20,985 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and COAD as cancer lineages where DDX31 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 DDX31 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DDX31 survival associations across molecular data types. DDX31 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) 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 DDX31 RNA expression–survival associations across cancer types. High DDX31 expression shows unfavorable associations in ACC, LIHC, BLCA and KIRC, but favorable associations in SCLC and GBM. The ACC 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 ACC as the clearest survival context for DDX31 RNA expression.
This table summarizes DDX31 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 9. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for DDX31. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DDX31 shows lower tumor expression in THCA and higher tumor expression in COAD, HNSC, STAD, LIHC and BLCA. The COAD box plot shows higher DDX31 RNA expression in tumor versus normal tissue (log2 FC = +1.348, t-test p < 0.001).
This table shows molecular features associated with DDX31 in patient tissues and cancer cell lines. In patient samples, DDX31 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, DDX31 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and LARGE_INTESTINE.
