Q-omics provides the consensus-scored DHX9 profile across patient tissues and cancer cell-line models. DHX9 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, DHX9 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, DHX9 protein abundance shows 33,941 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, HNSC, and LSCC as cancer lineages where DHX9 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 DHX9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DHX9 survival associations across molecular data types. DHX9 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DHX9 RNA expression–survival associations across cancer types. High DHX9 expression shows unfavorable associations in ACC, LIHC, MESO and KIRP, but favorable associations in KIRC and SCLC. 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 DHX9 RNA expression.
This table summarizes DHX9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for DHX9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DHX9 shows lower tumor expression in THCA and higher tumor expression in HNSC, COAD, LIHC, BLCA and STAD. The HNSC box plot shows higher DHX9 RNA expression in tumor versus normal tissue (log2 FC = +0.905, t-test p < 0.001).
This table shows molecular features associated with DHX9 in patient tissues and cancer cell lines. In patient samples, DHX9 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, DHX9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in STOMACH, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BONE.
