Q-omics provides the consensus-scored ATP1B3 profile across patient tissues and cancer cell-line models. ATP1B3 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ATP1B3 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, ATP1B3 protein abundance shows 22,744 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, HNSC, and LSCC as cancer lineages where ATP1B3 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 ATP1B3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP1B3 survival associations across molecular data types. ATP1B3 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (4) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP1B3 RNA expression–survival associations across cancer types. High ATP1B3 expression shows unfavorable associations in UVM, MESO, LIHC, ACC and KICH, but favorable associations in UCEC. 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 ATP1B3 RNA expression.
This table summarizes ATP1B3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 4. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for ATP1B3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP1B3 shows lower tumor expression in COAD, KICH and LUAD and higher tumor expression in HNSC, LIHC and STAD. The HNSC box plot shows higher ATP1B3 RNA expression in tumor versus normal tissue (log2 FC = +1.931, t-test p < 0.001).
This table shows molecular features associated with ATP1B3 in patient tissues and cancer cell lines. In patient samples, ATP1B3 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP1B3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BREAST.
