Q-omics provides the consensus-scored ATP9A profile across patient tissues and cancer cell-line models. ATP9A expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ATP9A is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, ATP9A protein abundance shows 34,257 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, KIRC, and GBM as cancer lineages where ATP9A 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 ATP9A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP9A survival associations across molecular data types. ATP9A RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP9A RNA expression–survival associations across cancer types. High ATP9A expression shows unfavorable associations in UVM, LUSC and CESC, but favorable associations in LGG, PAAD and COAD. The UVM 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 UVM as the clearest survival context for ATP9A RNA expression.
This table summarizes ATP9A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 11. The strongest signals are observed in KIRC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for ATP9A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP9A shows lower tumor expression in KIRC and THCA and higher tumor expression in LIHC, KIRP, STAD and CHOL. The KIRC box plot shows higher ATP9A RNA expression in normal versus tumor tissue (log2 FC = −1.090, t-test p < 0.001).
This table shows molecular features associated with ATP9A in patient tissues and cancer cell lines. In patient samples, ATP9A 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, ATP9A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and CNS.
