Q-omics provides the consensus-scored ATP13A4 profile across patient tissues and cancer cell-line models. ATP13A4 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, ATP13A4 is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, ATP13A4 RNA expression shows 19,392 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight KIRP, HNSC, and LUAD as cancer lineages where ATP13A4 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 ATP13A4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP13A4 survival associations across molecular data types. ATP13A4 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (7) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP13A4 RNA expression–survival associations across cancer types. High ATP13A4 expression shows unfavorable associations in UCEC and BLCA, but favorable associations in KIRP, KIRC, HNSC and LUAD. The KIRP 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 KIRP as the clearest survival context for ATP13A4 RNA expression.
This table summarizes ATP13A4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 4. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for ATP13A4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP13A4 shows lower tumor expression in HNSC, LUAD, COAD, LUSC, BRCA and KICH. The HNSC box plot shows higher ATP13A4 RNA expression in normal versus tumor tissue (log2 FC = −2.585, t-test p < 0.001).
This table shows molecular features associated with ATP13A4 in patient tissues and cancer cell lines. In patient samples, ATP13A4 shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP13A4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
