ATP synthase membrane subunit g likeGenealiases: ATP5K2 · ATP5L2
Q-omics provides the consensus-scored ATP5MGL profile across patient tissues and cancer cell-line models. ATP5MGL expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ATP5MGL is differentially expressed in 4, with the highest sampling consensus in LIHC. Additionally, ATP5MGL RNA expression shows 7,778 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and LIHC as cancer lineages where ATP5MGL 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 ATP5MGL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP5MGL survival associations across molecular data types. ATP5MGL RNA expression shows survival associations in the most cancer types (24), followed by mutation status (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP5MGL RNA expression–survival associations across cancer types. High ATP5MGL expression shows unfavorable associations in ACC, LIHC and DLBC, but favorable associations in SKCM, BLCA and LUAD. The ACC 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 ACC as the clearest survival context for ATP5MGL RNA expression.
This table summarizes ATP5MGL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 4. The strongest signals are observed in LIHC for RNA.
This table ranks reproducible tumor–normal expression differences for ATP5MGL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP5MGL shows lower tumor expression in BRCA and COAD and higher tumor expression in LIHC and HNSC. The LIHC box plot shows higher ATP5MGL RNA expression in tumor versus normal tissue (log2 FC = +0.059, t-test p < 0.001).
This table shows molecular features associated with ATP5MGL in patient tissues and cancer cell lines. In patient samples, ATP5MGL 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, ATP5MGL 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 LIVER and BLOOD_Leukemia.
