Q-omics provides the consensus-scored APOM profile across patient tissues and cancer cell-line models. APOM 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, APOM is differentially expressed in 12, with the highest sampling consensus in KIRP. Additionally, APOM protein abundance shows 18,776 significant protein co-abundance associations, with the highest sampling consensus in CCRCC. Together, these results highlight UVM, KIRP, and CCRCC as cancer lineages where APOM 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 APOM — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APOM survival associations across molecular data types. APOM RNA expression shows survival associations in the most cancer types (24), followed by mutation status (1) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APOM RNA expression–survival associations across cancer types. High APOM expression shows unfavorable associations in UCEC, but favorable associations in UVM, KIRC, MESO, BRCA and KIRP. 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 APOM RNA expression.
This table summarizes APOM tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRP for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for APOM. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APOM shows lower tumor expression in KIRP, KICH, THCA, CHOL and BRCA and higher tumor expression in COAD. The KIRP box plot shows higher APOM RNA expression in normal versus tumor tissue (log2 FC = −2.841, t-test p < 0.001).
This table shows molecular features associated with APOM in patient tissues and cancer cell lines. In patient samples, APOM shows the broadest associations at the RNA and protein expression levels, with CCRCC recurring as the lineage with the largest associated feature set. In cancer cell lines, APOM RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BONE.
