Q-omics provides the consensus-scored ACOX2 profile across patient tissues and cancer cell-line models. ACOX2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ACOX2 is differentially expressed in 13, with the highest sampling consensus in BLCA. Additionally, ACOX2 protein abundance shows 22,370 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, BLCA, and LSCC as cancer lineages where ACOX2 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 ACOX2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ACOX2 survival associations across molecular data types. ACOX2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (8) 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 ACOX2 RNA expression–survival associations across cancer types. High ACOX2 expression shows unfavorable associations in BLCA, but favorable associations in KIRC, MESO, HNSC, LIHC and SKCM. The KIRC 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 KIRC as the clearest survival context for ACOX2 RNA expression.
This table summarizes ACOX2 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 BLCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ACOX2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ACOX2 shows lower tumor expression in BLCA, KIRP, HNSC, LUSC, LIHC and KICH. The BLCA box plot shows higher ACOX2 RNA expression in normal versus tumor tissue (log2 FC = −3.420, t-test p < 0.001).
This table shows molecular features associated with ACOX2 in patient tissues and cancer cell lines. In patient samples, ACOX2 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, ACOX2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and BONE.
