Q-omics provides the consensus-scored ATP6V0D2 profile across patient tissues and cancer cell-line models. ATP6V0D2 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ATP6V0D2 is differentially expressed in 16, with the highest sampling consensus in KIRC. Additionally, ATP6V0D2 RNA expression shows 10,856 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, KIRC, and GBM as cancer lineages where ATP6V0D2 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 ATP6V0D2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP6V0D2 survival associations across molecular data types. ATP6V0D2 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (4) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP6V0D2 RNA expression–survival associations across cancer types. High ATP6V0D2 expression shows unfavorable associations in UVM, ACC, LIHC and BLCA, but favorable associations in UCEC and LUAD. The UVM 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 UVM as the clearest survival context for ATP6V0D2 RNA expression.
This table summarizes ATP6V0D2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ATP6V0D2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP6V0D2 shows lower tumor expression in KIRC, KIRP and COAD and higher tumor expression in HNSC, LIHC and STAD. The KIRC box plot shows higher ATP6V0D2 RNA expression in normal versus tumor tissue (log2 FC = −6.049, t-test p < 0.001).
This table shows molecular features associated with ATP6V0D2 in patient tissues and cancer cell lines. In patient samples, ATP6V0D2 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, ATP6V0D2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and BLOOD_Leukemia.
