Q-omics provides the consensus-scored ATP6V1G1 profile across patient tissues and cancer cell-line models. ATP6V1G1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, ATP6V1G1 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, ATP6V1G1 RNA expression shows 18,948 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight HNSC, and ACC as cancer lineages where ATP6V1G1 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 ATP6V1G1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP6V1G1 survival associations across molecular data types. ATP6V1G1 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) and 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 ATP6V1G1 RNA expression–survival associations across cancer types. High ATP6V1G1 expression shows unfavorable associations in HNSC, ACC, UVM and ESCA, but favorable associations in KIRC and UCEC. The HNSC 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 HNSC as the clearest survival context for ATP6V1G1 RNA expression.
This table summarizes ATP6V1G1 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 6. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ATP6V1G1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP6V1G1 shows lower tumor expression in KIRC and KICH and higher tumor expression in HNSC, LIHC, BRCA and COAD. The HNSC box plot shows higher ATP6V1G1 RNA expression in tumor versus normal tissue (log2 FC = +0.597, t-test p < 0.001).
This table shows molecular features associated with ATP6V1G1 in patient tissues and cancer cell lines. In patient samples, ATP6V1G1 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, ATP6V1G1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BREAST.
