Q-omics provides the consensus-scored ATP6V1F profile across patient tissues and cancer cell-line models. ATP6V1F expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, ATP6V1F is differentially expressed in 15, with the highest sampling consensus in COAD. Additionally, ATP6V1F protein abundance shows 25,157 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LIHC, COAD, and GBM as cancer lineages where ATP6V1F 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 ATP6V1F — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP6V1F survival associations across molecular data types. ATP6V1F RNA expression shows survival associations in the most cancer types (24), followed by mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP6V1F RNA expression–survival associations across cancer types. High ATP6V1F expression shows unfavorable associations in LIHC, KICH, HNSC, LGG and ACC, but favorable associations in DLBC. The LIHC 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 LIHC as the clearest survival context for ATP6V1F RNA expression.
This table summarizes ATP6V1F 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 6. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ATP6V1F. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP6V1F shows higher tumor expression in COAD, HNSC, LIHC, BLCA, KIRP and THCA. The COAD box plot shows higher ATP6V1F RNA expression in tumor versus normal tissue (log2 FC = +1.467, t-test p < 0.001).
This table shows molecular features associated with ATP6V1F in patient tissues and cancer cell lines. In patient samples, ATP6V1F shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP6V1F RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in SKIN and PANCREAS.
