Q-omics provides the consensus-scored CDK9 profile across patient tissues and cancer cell-line models. CDK9 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, CDK9 is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, CDK9 protein abundance shows 28,228 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, HNSC, and LSCC as cancer lineages where CDK9 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 CDK9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CDK9 survival associations across molecular data types. CDK9 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) 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 CDK9 RNA expression–survival associations across cancer types. High CDK9 expression shows unfavorable associations in KIRC, COAD, ACC, UVM and HNSC, but favorable associations in OV. The KIRC 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 KIRC as the clearest survival context for CDK9 RNA expression.
This table summarizes CDK9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 8. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for CDK9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CDK9 shows lower tumor expression in KICH and higher tumor expression in HNSC, KIRC, COAD, THCA and STAD. The HNSC box plot shows higher CDK9 RNA expression in tumor versus normal tissue (log2 FC = +1.174, t-test p < 0.001).
This table shows molecular features associated with CDK9 in patient tissues and cancer cell lines. In patient samples, CDK9 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, CDK9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and LUNG_NSCLC_LUAD.
