Q-omics provides the consensus-scored DHX16 profile across patient tissues and cancer cell-line models. DHX16 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in COAD. Among the 18 cancer types available for tumor–normal comparison, DHX16 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, DHX16 protein abundance shows 26,970 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight COAD, HNSC, and GBM as cancer lineages where DHX16 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 DHX16 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DHX16 survival associations across molecular data types. DHX16 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DHX16 RNA expression–survival associations across cancer types. High DHX16 expression shows unfavorable associations in COAD, ACC, LIHC and LGG, but favorable associations in READ and UCEC. The COAD 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 COAD as the clearest survival context for DHX16 RNA expression.
This table summarizes DHX16 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 6. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for DHX16. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DHX16 shows lower tumor expression in KICH and higher tumor expression in HNSC, COAD, LIHC, STAD and BLCA. The HNSC box plot shows higher DHX16 RNA expression in tumor versus normal tissue (log2 FC = +0.804, t-test p < 0.001).
This table shows molecular features associated with DHX16 in patient tissues and cancer cell lines. In patient samples, DHX16 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, DHX16 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
