Q-omics provides the consensus-scored ATP2B1 profile across patient tissues and cancer cell-line models. ATP2B1 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in LUAD. Among the 18 cancer types available for tumor–normal comparison, ATP2B1 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, ATP2B1 protein abundance shows 24,054 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LUAD, HNSC, and GBM as cancer lineages where ATP2B1 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 ATP2B1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP2B1 survival associations across molecular data types. ATP2B1 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (9) 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 ATP2B1 RNA expression–survival associations across cancer types. High ATP2B1 expression shows unfavorable associations in LUAD, LIHC, UVM and LGG, but favorable associations in UCS and KIRC. The LUAD Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .004). Together, the overview and detailed table identify LUAD as the clearest survival context for ATP2B1 RNA expression.
This table summarizes ATP2B1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for ATP2B1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP2B1 shows lower tumor expression in COAD and THCA and higher tumor expression in HNSC, STAD, LIHC and LUSC. The HNSC box plot shows higher ATP2B1 RNA expression in tumor versus normal tissue (log2 FC = +1.397, t-test p < 0.001).
This table shows molecular features associated with ATP2B1 in patient tissues and cancer cell lines. In patient samples, ATP2B1 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, ATP2B1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BONE.
