Q-omics provides the consensus-scored ACACB profile across patient tissues and cancer cell-line models. ACACB expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ACACB is differentially expressed in 16, with the highest sampling consensus in THCA. Additionally, ACACB RNA expression shows 20,178 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, THCA, and ACC as cancer lineages where ACACB 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 ACACB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ACACB survival associations across molecular data types. ACACB RNA expression shows survival associations in the most cancer types (24), followed by mutation status (11) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ACACB RNA expression–survival associations across cancer types. High ACACB expression shows unfavorable associations in UVM, UCEC and ACC, but favorable associations in SCLC, HNSC and PAAD. The UVM 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 UVM as the clearest survival context for ACACB RNA expression.
This table summarizes ACACB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 4. The strongest signals are observed in THCA for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for ACACB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ACACB shows lower tumor expression in THCA, COAD, LUAD, KIRC, BLCA and LUSC. The THCA box plot shows higher ACACB RNA expression in normal versus tumor tissue (log2 FC = −2.502, t-test p < 0.001).
This table shows molecular features associated with ACACB in patient tissues and cancer cell lines. In patient samples, ACACB 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, ACACB 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 UPPER_AERODIGESTIVE_TRACT and LARGE_INTESTINE.
