Q-omics provides the consensus-scored ATP6V0A2 profile across patient tissues and cancer cell-line models. ATP6V0A2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, ATP6V0A2 is differentially expressed in 9, with the highest sampling consensus in LIHC. Additionally, ATP6V0A2 RNA expression shows 21,283 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight MESO, LIHC, and ACC as cancer lineages where ATP6V0A2 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 ATP6V0A2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP6V0A2 survival associations across molecular data types. ATP6V0A2 RNA expression shows survival associations in the most cancer types (22), 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 ATP6V0A2 RNA expression–survival associations across cancer types. High ATP6V0A2 expression shows unfavorable associations in MESO, ACC and LIHC, but favorable associations in HNSC, UCS and SCLC. The MESO 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 MESO as the clearest survival context for ATP6V0A2 RNA expression.
This table summarizes ATP6V0A2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 4. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ATP6V0A2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP6V0A2 shows higher tumor expression in LIHC, HNSC, STAD, KIRP, CHOL and BRCA. The LIHC box plot shows higher ATP6V0A2 RNA expression in tumor versus normal tissue (log2 FC = +0.580, t-test p < 0.001).
This table shows molecular features associated with ATP6V0A2 in patient tissues and cancer cell lines. In patient samples, ATP6V0A2 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, ATP6V0A2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Lymphoma.
