Q-omics provides the consensus-scored ATP6V1G2-DDX39B profile across patient tissues and cancer cell-line models. ATP6V1G2-DDX39B expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in DLBC. Among the 18 cancer types available for tumor–normal comparison, ATP6V1G2-DDX39B is differentially expressed in 5, with the highest sampling consensus in KICH. Additionally, ATP6V1G2-DDX39B RNA expression shows 11,868 significant gene co-expression associations, with the highest sampling consensus in PCPG. Together, these results highlight DLBC, KICH, and PCPG as cancer lineages where ATP6V1G2-DDX39B 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 ATP6V1G2-DDX39B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP6V1G2-DDX39B survival associations across molecular data types. ATP6V1G2-DDX39B RNA expression shows survival associations in the most cancer types (20). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP6V1G2-DDX39B RNA expression–survival associations across cancer types. High ATP6V1G2-DDX39B expression shows unfavorable associations in DLBC, CHOL and THCA, but favorable associations in LGG, GBM and BRCA. The DLBC 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 DLBC as the clearest survival context for ATP6V1G2-DDX39B RNA expression.
This table summarizes ATP6V1G2-DDX39B tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5. The strongest signals are observed in KICH for RNA.
This table ranks reproducible tumor–normal expression differences for ATP6V1G2-DDX39B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP6V1G2-DDX39B shows lower tumor expression in KICH and THCA and higher tumor expression in LUAD, CHOL and KIRC. The KICH box plot shows higher ATP6V1G2-DDX39B RNA expression in normal versus tumor tissue (log2 FC = −0.024, t-test p = .001).
This table shows molecular features associated with ATP6V1G2-DDX39B in patient tissues and cancer cell lines. In patient samples, ATP6V1G2-DDX39B shows the broadest associations at the RNA and protein expression levels, with PCPG recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP6V1G2-DDX39B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD.
