Q-omics provides the consensus-scored ACOT2 profile across patient tissues and cancer cell-line models. ACOT2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ACOT2 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, ACOT2 RNA expression shows 18,702 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, KIRC, and ACC as cancer lineages where ACOT2 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 ACOT2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ACOT2 survival associations across molecular data types. ACOT2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) 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 ACOT2 RNA expression–survival associations across cancer types. High ACOT2 expression shows unfavorable associations in UVM, LAML and READ, but favorable associations in BRCA, KIRC and MESO. The UVM Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify UVM as the clearest survival context for ACOT2 RNA expression.
This table summarizes ACOT2 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 4. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ACOT2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ACOT2 shows lower tumor expression in KIRC, COAD, LUSC, KIRP, LUAD and BRCA. The KIRC box plot shows higher ACOT2 RNA expression in normal versus tumor tissue (log2 FC = −0.956, t-test p < 0.001).
This table shows molecular features associated with ACOT2 in patient tissues and cancer cell lines. In patient samples, ACOT2 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, ACOT2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SOFT_TISSUE.
