Q-omics provides the consensus-scored ACCS profile across patient tissues and cancer cell-line models. ACCS expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, ACCS is differentially expressed in 7, with the highest sampling consensus in KIRC. Additionally, ACCS RNA expression shows 16,376 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight MESO, KIRC, and UVM as cancer lineages where ACCS 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 ACCS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ACCS survival associations across molecular data types. ACCS RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) 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 ACCS RNA expression–survival associations across cancer types. High ACCS expression shows unfavorable associations in KICH and LGG, but favorable associations in MESO, KIRP, READ and PAAD. The MESO Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify MESO as the clearest survival context for ACCS RNA expression.
This table summarizes ACCS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 2. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ACCS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ACCS shows lower tumor expression in BRCA and LUSC and higher tumor expression in KIRC, COAD, LIHC and CHOL. The KIRC box plot shows higher ACCS RNA expression in tumor versus normal tissue (log2 FC = +0.742, t-test p < 0.001).
This table shows molecular features associated with ACCS in patient tissues and cancer cell lines. In patient samples, ACCS shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, ACCS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
