Q-omics provides the consensus-scored ATP10A profile across patient tissues and cancer cell-line models. ATP10A expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ATP10A is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, ATP10A RNA expression shows 18,786 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, KICH, and LSCC as cancer lineages where ATP10A 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 ATP10A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP10A survival associations across molecular data types. ATP10A RNA expression shows survival associations in the most cancer types (22), followed by mutation status (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP10A RNA expression–survival associations across cancer types. High ATP10A expression shows unfavorable associations in UVM, ACC, LUSC, MESO and LAML, but favorable associations in HNSC. 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 ATP10A RNA expression.
This table summarizes ATP10A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11. The strongest signals are observed in KICH for RNA.
This table ranks reproducible tumor–normal expression differences for ATP10A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP10A shows lower tumor expression in KICH, LUSC and LUAD and higher tumor expression in CHOL, THCA and LIHC. The KICH box plot shows higher ATP10A RNA expression in normal versus tumor tissue (log2 FC = −2.013, t-test p < 0.001).
This table shows molecular features associated with ATP10A in patient tissues and cancer cell lines. In patient samples, ATP10A 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, ATP10A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BONE.
