Q-omics provides the consensus-scored ATP6V0C profile across patient tissues and cancer cell-line models. ATP6V0C expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ATP6V0C is differentially expressed in 10, with the highest sampling consensus in LIHC. Additionally, ATP6V0C RNA expression shows 18,933 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, LIHC, and ACC as cancer lineages where ATP6V0C 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 ATP6V0C — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP6V0C survival associations across molecular data types. ATP6V0C RNA expression shows survival associations in the most cancer types (19), followed by mutation status (2) 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 ATP6V0C RNA expression–survival associations across cancer types. High ATP6V0C expression shows unfavorable associations in LIHC, UCS, LUSC, STAD and BLCA, but favorable associations in KIRC. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for ATP6V0C RNA expression.
This table summarizes ATP6V0C tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 3. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ATP6V0C. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP6V0C shows lower tumor expression in KIRC and COAD and higher tumor expression in LIHC, KICH, BRCA and KIRP. The LIHC box plot shows higher ATP6V0C RNA expression in tumor versus normal tissue (log2 FC = +1.013, t-test p < 0.001).
This table shows molecular features associated with ATP6V0C in patient tissues and cancer cell lines. In patient samples, ATP6V0C 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, ATP6V0C 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 SOFT_TISSUE and BLOOD_Leukemia.
