Q-omics provides the consensus-scored ATP6V0E1P1 profile across patient tissues and cancer cell-line models. ATP6V0E1P1 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in TGCT. Among the 18 cancer types available for tumor–normal comparison, ATP6V0E1P1 is differentially expressed in 2, with the highest sampling consensus in THCA. Additionally, ATP6V0E1P1 RNA expression shows 6,575 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight TGCT, THCA, and STAD as cancer lineages where ATP6V0E1P1 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 ATP6V0E1P1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP6V0E1P1 survival associations across molecular data types. ATP6V0E1P1 RNA expression shows survival associations in the most cancer types (21). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP6V0E1P1 RNA expression–survival associations across cancer types. High ATP6V0E1P1 expression shows unfavorable associations in TGCT, UCEC, SKCM and KICH, but favorable associations in LUAD and BLCA. The TGCT 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 TGCT as the clearest survival context for ATP6V0E1P1 RNA expression.
This table summarizes ATP6V0E1P1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 2. The strongest signals are observed in THCA for RNA.
This table ranks reproducible tumor–normal expression differences for ATP6V0E1P1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP6V0E1P1 shows lower tumor expression in THCA and higher tumor expression in KIRC. The THCA box plot shows higher ATP6V0E1P1 RNA expression in normal versus tumor tissue (log2 FC = −0.088, t-test p = .039).
This table shows molecular features associated with ATP6V0E1P1 in patient tissues and cancer cell lines. In patient samples, ATP6V0E1P1 shows the broadest associations at the RNA and protein expression levels, with STAD recurring as the lineage with the largest associated feature set.
