Q-omics provides the consensus-scored ATP6V0B profile across patient tissues and cancer cell-line models. ATP6V0B expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ATP6V0B is differentially expressed in 13, with the highest sampling consensus in BLCA. Additionally, ATP6V0B RNA expression shows 19,608 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and BLCA as cancer lineages where ATP6V0B 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 ATP6V0B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP6V0B survival associations across molecular data types. ATP6V0B RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) 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 ATP6V0B RNA expression–survival associations across cancer types. High ATP6V0B expression shows unfavorable associations in ACC, UVM, KICH, LIHC, UCS and LGG. 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 ATP6V0B RNA expression.
This table summarizes ATP6V0B 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 KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ATP6V0B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP6V0B shows lower tumor expression in KIRC and higher tumor expression in BLCA, HNSC, LIHC, BRCA and STAD. The BLCA box plot shows higher ATP6V0B RNA expression in tumor versus normal tissue (log2 FC = +1.032, t-test p < 0.001).
This table shows molecular features associated with ATP6V0B in patient tissues and cancer cell lines. In patient samples, ATP6V0B shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP6V0B 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 SKIN and BLOOD_Leukemia.
