Q-omics provides the consensus-scored ATP6AP2 profile across patient tissues and cancer cell-line models. ATP6AP2 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ATP6AP2 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, ATP6AP2 RNA expression shows 19,836 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, HNSC, and UVM as cancer lineages where ATP6AP2 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 ATP6AP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP6AP2 survival associations across molecular data types. ATP6AP2 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (3) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP6AP2 RNA expression–survival associations across cancer types. High ATP6AP2 expression shows unfavorable associations in LIHC, UVM, LGG, MESO and GBM, 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 ATP6AP2 RNA expression.
This table summarizes ATP6AP2 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 HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ATP6AP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP6AP2 shows lower tumor expression in THCA and higher tumor expression in HNSC, LIHC, KICH, BRCA and STAD. The HNSC box plot shows higher ATP6AP2 RNA expression in tumor versus normal tissue (log2 FC = +0.936, t-test p < 0.001).
This table shows molecular features associated with ATP6AP2 in patient tissues and cancer cell lines. In patient samples, ATP6AP2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP6AP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Lymphoma.
