Q-omics provides the consensus-scored ATP11AUN profile across patient tissues and cancer cell-line models. ATP11AUN expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ATP11AUN is differentially expressed in 10, with the highest sampling consensus in KICH. Additionally, ATP11AUN RNA expression shows 8,582 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KICH as cancer lineages where ATP11AUN 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 ATP11AUN — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP11AUN survival associations across molecular data types. ATP11AUN RNA expression shows survival associations in the most cancer types (21), followed by mutation status (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP11AUN RNA expression–survival associations across cancer types. High ATP11AUN expression shows unfavorable associations in ACC, COAD and CHOL, but favorable associations in KIRC, UVM and STAD. 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 ATP11AUN RNA expression.
This table summarizes ATP11AUN tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10. The strongest signals are observed in KICH for RNA.
This table ranks reproducible tumor–normal expression differences for ATP11AUN. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP11AUN shows lower tumor expression in KICH, KIRP and LUSC and higher tumor expression in COAD, HNSC and UCEC. The KICH box plot shows higher ATP11AUN RNA expression in normal versus tumor tissue (log2 FC = −0.275, t-test p < 0.001).
This table shows molecular features associated with ATP11AUN in patient tissues and cancer cell lines. In patient samples, ATP11AUN 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, ATP11AUN RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC.
