Q-omics provides the consensus-scored ATP6V1C2 profile across patient tissues and cancer cell-line models. ATP6V1C2 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, ATP6V1C2 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, ATP6V1C2 RNA expression shows 18,307 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, and UVM as cancer lineages where ATP6V1C2 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 ATP6V1C2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP6V1C2 survival associations across molecular data types. ATP6V1C2 RNA expression shows survival associations in the most cancer types (19), followed by mutation status (4) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP6V1C2 RNA expression–survival associations across cancer types. High ATP6V1C2 expression shows unfavorable associations in KIRC, KIRP, ACC, UVM and LIHC, but favorable associations in SCLC. The KIRC 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 KIRC as the clearest survival context for ATP6V1C2 RNA expression.
This table summarizes ATP6V1C2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, 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 ATP6V1C2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP6V1C2 shows lower tumor expression in KIRC, KIRP and HNSC and higher tumor expression in COAD, LUAD and UCEC. The KIRC box plot shows higher ATP6V1C2 RNA expression in normal versus tumor tissue (log2 FC = −3.157, t-test p < 0.001).
This table shows molecular features associated with ATP6V1C2 in patient tissues and cancer cell lines. In patient samples, ATP6V1C2 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, ATP6V1C2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and LUNG_SCLC.
