Q-omics provides the consensus-scored ATP23 profile across patient tissues and cancer cell-line models. ATP23 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ATP23 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, ATP23 RNA expression shows 19,347 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KIRC as cancer lineages where ATP23 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 ATP23 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATP23 survival associations across molecular data types. ATP23 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ATP23 RNA expression–survival associations across cancer types. High ATP23 expression shows unfavorable associations in ACC, UVM, KIRP, LGG and MESO, but favorable associations in LUSC. 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 ATP23 RNA expression.
This table summarizes ATP23 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 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ATP23. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATP23 shows lower tumor expression in COAD and KICH and higher tumor expression in KIRC, HNSC, LUSC and UCEC. The KIRC box plot shows higher ATP23 RNA expression in tumor versus normal tissue (log2 FC = +0.589, t-test p < 0.001).
This table shows molecular features associated with ATP23 in patient tissues and cancer cell lines. In patient samples, ATP23 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, ATP23 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
