Q-omics provides the consensus-scored ATP2A2 profile across patient tissues and cancer cell-line models. ATP2A2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ATP2A2 is differentially expressed in 11, with the highest sampling consensus in KIRP. Additionally, ATP2A2 protein abundance shows 24,475 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, KIRP, and GBM as cancer lineages where ATP2A2 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 ATP2A2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP2A2 survival associations across molecular data types. ATP2A2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) 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 ATP2A2 RNA expression–survival associations across cancer types. High ATP2A2 expression shows unfavorable associations in UVM, MESO, ACC, HNSC and PAAD, but favorable associations in UCS. The UVM 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 UVM as the clearest survival context for ATP2A2 RNA expression.
This table summarizes ATP2A2 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 7. The strongest signals are observed in KIRP for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ATP2A2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP2A2 shows higher tumor expression in KIRP, STAD, KIRC, LUAD, LUSC and LIHC. The KIRP box plot shows higher ATP2A2 RNA expression in tumor versus normal tissue (log2 FC = +1.043, t-test p < 0.001).
This table shows molecular features associated with ATP2A2 in patient tissues and cancer cell lines. In patient samples, ATP2A2 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, ATP2A2 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_Lymphoma and SKIN.
