Q-omics provides the consensus-scored ATP13A3 profile across patient tissues and cancer cell-line models. ATP13A3 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, ATP13A3 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, ATP13A3 protein abundance shows 23,928 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KICH, HNSC, and PDAC as cancer lineages where ATP13A3 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 ATP13A3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP13A3 survival associations across molecular data types. ATP13A3 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (7) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP13A3 RNA expression–survival associations across cancer types. High ATP13A3 expression shows unfavorable associations in KICH, ACC, PAAD, LGG and KIRP, but favorable associations in SKCM. The KICH Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify KICH as the clearest survival context for ATP13A3 RNA expression.
This table summarizes ATP13A3 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 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ATP13A3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP13A3 shows lower tumor expression in THCA and KICH and higher tumor expression in HNSC, KIRC, STAD and BLCA. The HNSC box plot shows higher ATP13A3 RNA expression in tumor versus normal tissue (log2 FC = +1.483, t-test p < 0.001).
This table shows molecular features associated with ATP13A3 in patient tissues and cancer cell lines. In patient samples, ATP13A3 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP13A3 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 BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
