Q-omics provides the consensus-scored ACSF2 profile across patient tissues and cancer cell-line models. ACSF2 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ACSF2 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, ACSF2 protein abundance shows 21,115 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, KIRC, and LSCC as cancer lineages where ACSF2 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 ACSF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ACSF2 survival associations across molecular data types. ACSF2 RNA expression shows survival associations in the most cancer types (28), 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 ACSF2 RNA expression–survival associations across cancer types. High ACSF2 expression shows unfavorable associations in KICH, but favorable associations in UVM, BRCA, MESO, ACC and LUAD. The UVM 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 UVM as the clearest survival context for ACSF2 RNA expression.
This table summarizes ACSF2 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 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ACSF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ACSF2 shows lower tumor expression in KIRC, COAD, KIRP, THCA, KICH and READ. The KIRC box plot shows higher ACSF2 RNA expression in normal versus tumor tissue (log2 FC = −2.419, t-test p < 0.001).
This table shows molecular features associated with ACSF2 in patient tissues and cancer cell lines. In patient samples, ACSF2 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ACSF2 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 STOMACH and BLOOD_Leukemia.
