Q-omics provides the consensus-scored ATP9B profile across patient tissues and cancer cell-line models. ATP9B expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, ATP9B is differentially expressed in 12, with the highest sampling consensus in THCA. Additionally, ATP9B RNA expression shows 21,681 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight HNSC, THCA, and ACC as cancer lineages where ATP9B 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 ATP9B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP9B survival associations across molecular data types. ATP9B RNA expression shows survival associations in the most cancer types (28), followed by mutation status (8) 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 ATP9B RNA expression–survival associations across cancer types. High ATP9B expression shows unfavorable associations in ACC, LGG and MESO, but favorable associations in HNSC, KIRC and BRCA. 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 ATP9B RNA expression.
This table summarizes ATP9B tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 4. The strongest signals are observed in THCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ATP9B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP9B shows lower tumor expression in THCA, BRCA, LUSC and BLCA and higher tumor expression in LIHC and CHOL. The THCA box plot shows higher ATP9B RNA expression in normal versus tumor tissue (log2 FC = −0.585, t-test p < 0.001).
This table shows molecular features associated with ATP9B in patient tissues and cancer cell lines. In patient samples, ATP9B shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP9B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
