Q-omics provides the consensus-scored ATP2A1 profile across patient tissues and cancer cell-line models. ATP2A1 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ATP2A1 is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, ATP2A1 RNA expression shows 18,718 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight ACC, COAD, and UVM as cancer lineages where ATP2A1 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 ATP2A1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP2A1 survival associations across molecular data types. ATP2A1 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (9) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP2A1 RNA expression–survival associations across cancer types. High ATP2A1 expression shows unfavorable associations in ACC, COAD, KIRC, MESO, LGG and UVM. The ACC 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 ACC as the clearest survival context for ATP2A1 RNA expression.
This table summarizes ATP2A1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 2. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ATP2A1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP2A1 shows lower tumor expression in HNSC and higher tumor expression in COAD, KIRC, LUAD, LIHC and STAD. The COAD box plot shows higher ATP2A1 RNA expression in tumor versus normal tissue (log2 FC = +0.589, t-test p < 0.001).
This table shows molecular features associated with ATP2A1 in patient tissues and cancer cell lines. In patient samples, ATP2A1 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP2A1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
