Q-omics provides the consensus-scored APOC4-APOC2 profile across patient tissues and cancer cell-line models. APOC4-APOC2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, APOC4-APOC2 is differentially expressed in 14, with the highest sampling consensus in LUAD. Additionally, APOC4-APOC2 RNA expression shows 10,918 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight ACC, LUAD, and UVM as cancer lineages where APOC4-APOC2 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 APOC4-APOC2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APOC4-APOC2 survival associations across molecular data types. APOC4-APOC2 RNA expression shows survival associations in the most cancer types (25). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APOC4-APOC2 RNA expression–survival associations across cancer types. High APOC4-APOC2 expression shows unfavorable associations in ACC, LGG, KIRC and KICH, but favorable associations in BLCA and LUAD. The ACC 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 ACC as the clearest survival context for APOC4-APOC2 RNA expression.
This table summarizes APOC4-APOC2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14. The strongest signals are observed in THCA for RNA.
This table ranks reproducible tumor–normal expression differences for APOC4-APOC2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APOC4-APOC2 shows lower tumor expression in LUAD and CHOL and higher tumor expression in THCA, HNSC, LIHC and BRCA. The LUAD box plot shows higher APOC4-APOC2 RNA expression in normal versus tumor tissue (log2 FC = −0.623, t-test p < 0.001).
This table shows molecular features associated with APOC4-APOC2 in patient tissues and cancer cell lines. In patient samples, APOC4-APOC2 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, APOC4-APOC2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia.
