Q-omics provides the consensus-scored ATP1A3 profile across patient tissues and cancer cell-line models. ATP1A3 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in OV. Among the 18 cancer types available for tumor–normal comparison, ATP1A3 is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, ATP1A3 protein abundance shows 34,946 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight OV, KIRC, and GBM as cancer lineages where ATP1A3 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 ATP1A3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP1A3 survival associations across molecular data types. ATP1A3 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (8) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP1A3 RNA expression–survival associations across cancer types. High ATP1A3 expression shows unfavorable associations in OV, KIRC, KIRP, ACC and MESO, but favorable associations in LIHC. The OV 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 OV as the clearest survival context for ATP1A3 RNA expression.
This table summarizes ATP1A3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ATP1A3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP1A3 shows higher tumor expression in KIRC, KICH, KIRP, BRCA, BLCA and LUAD. The KIRC box plot shows higher ATP1A3 RNA expression in tumor versus normal tissue (log2 FC = +0.513, t-test p < 0.001).
This table shows molecular features associated with ATP1A3 in patient tissues and cancer cell lines. In patient samples, ATP1A3 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, ATP1A3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and BLOOD_Leukemia.
