Q-omics provides the consensus-scored APOC4 profile across patient tissues and cancer cell-line models. APOC4 expression is associated with patient survival in 16 of 34 cancer types, with the highest sampling consensus in TGCT. Among the 18 cancer types available for tumor–normal comparison, APOC4 is differentially expressed in 8, with the highest sampling consensus in CHOL. Additionally, APOC4 protein abundance shows 20,239 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight TGCT, CHOL, and LUAD as cancer lineages where APOC4 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 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APOC4 survival associations across molecular data types. APOC4 RNA expression shows survival associations in the most cancer types (16), followed by mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APOC4 RNA expression–survival associations across cancer types. High APOC4 expression shows unfavorable associations in TGCT, KIRC, CHOL, LUSC, BRCA and UCS. The TGCT 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 TGCT as the clearest survival context for APOC4 RNA expression.
This table summarizes APOC4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 7. The strongest signals are observed in CHOL for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for APOC4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APOC4 shows lower tumor expression in CHOL, LIHC, THCA and COAD and higher tumor expression in UCEC and PRAD. The CHOL box plot shows higher APOC4 RNA expression in normal versus tumor tissue (log2 FC = −0.867, t-test p < 0.001).
This table shows molecular features associated with APOC4 in patient tissues and cancer cell lines. In patient samples, APOC4 shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, APOC4 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 LUNG_SCLC and SKIN.
