Q-omics provides the consensus-scored DDX17 profile across patient tissues and cancer cell-line models. DDX17 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, DDX17 is differentially expressed in 9, with the highest sampling consensus in LIHC. Additionally, DDX17 protein abundance shows 28,934 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, LIHC, and GBM as cancer lineages where DDX17 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 DDX17 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DDX17 survival associations across molecular data types. DDX17 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (5) 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 DDX17 RNA expression–survival associations across cancer types. High DDX17 expression shows unfavorable associations in ACC, UVM and LIHC, but favorable associations in SKCM, READ 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 DDX17 RNA expression.
This table summarizes DDX17 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 7. The strongest signals are observed in LIHC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for DDX17. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DDX17 shows lower tumor expression in KICH, THCA and BRCA and higher tumor expression in LIHC, HNSC and CHOL. The LIHC box plot shows higher DDX17 RNA expression in tumor versus normal tissue (log2 FC = +0.799, t-test p < 0.001).
This table shows molecular features associated with DDX17 in patient tissues and cancer cell lines. In patient samples, DDX17 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, DDX17 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SOFT_TISSUE.
