Q-omics provides the consensus-scored ATP13A1 profile across patient tissues and cancer cell-line models. ATP13A1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ATP13A1 is differentially expressed in 16, with the highest sampling consensus in BLCA. Additionally, ATP13A1 protein abundance shows 25,456 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, BLCA, and GBM as cancer lineages where ATP13A1 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 ATP13A1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP13A1 survival associations across molecular data types. ATP13A1 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (6) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP13A1 RNA expression–survival associations across cancer types. High ATP13A1 expression shows unfavorable associations in KIRC, ACC, LIHC, UVM, BLCA and KICH. The KIRC 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 KIRC as the clearest survival context for ATP13A1 RNA expression.
This table summarizes ATP13A1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRP for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ATP13A1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP13A1 shows higher tumor expression in BLCA, COAD, KIRP, HNSC, LIHC and STAD. The BLCA box plot shows higher ATP13A1 RNA expression in tumor versus normal tissue (log2 FC = +1.451, t-test p < 0.001).
This table shows molecular features associated with ATP13A1 in patient tissues and cancer cell lines. In patient samples, ATP13A1 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, ATP13A1 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 BLOOD_Lymphoma and BLOOD_Leukemia.
