Q-omics provides the consensus-scored CUL9 profile across patient tissues and cancer cell-line models. CUL9 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, CUL9 is differentially expressed in 7, with the highest sampling consensus in LIHC. Additionally, CUL9 protein abundance shows 32,499 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight HNSC, LIHC, and GBM as cancer lineages where CUL9 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 CUL9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CUL9 survival associations across molecular data types. CUL9 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CUL9 RNA expression–survival associations across cancer types. High CUL9 expression shows unfavorable associations in KICH, ACC, COAD and MESO, but favorable associations in HNSC and LUAD. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify HNSC as the clearest survival context for CUL9 RNA expression.
This table summarizes CUL9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 9. The strongest signals are observed in LIHC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for CUL9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CUL9 shows lower tumor expression in THCA and KICH and higher tumor expression in LIHC, CHOL, COAD and STAD. The LIHC box plot shows higher CUL9 RNA expression in tumor versus normal tissue (log2 FC = +1.479, t-test p < 0.001).
This table shows molecular features associated with CUL9 in patient tissues and cancer cell lines. In patient samples, CUL9 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, CUL9 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_Lymphoma and BLOOD_Leukemia.
