Q-omics provides the consensus-scored APOE profile across patient tissues and cancer cell-line models. APOE expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in THCA. Among the 18 cancer types available for tumor–normal comparison, APOE is differentially expressed in 9, with the highest sampling consensus in KIRC. Additionally, APOE protein abundance shows 17,341 significant protein co-abundance associations, with the highest sampling consensus in COAD. Together, these results highlight THCA, KIRC, and COAD as cancer lineages where APOE 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 APOE — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APOE survival associations across molecular data types. APOE RNA expression shows survival associations in the most cancer types (26), followed by mutation status (7) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APOE RNA expression–survival associations across cancer types. High APOE expression shows unfavorable associations in ACC, UCS and BLCA, but favorable associations in THCA, UVM and DLBC. The THCA 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 THCA as the clearest survival context for APOE RNA expression.
This table summarizes APOE tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for APOE. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APOE shows lower tumor expression in CHOL and higher tumor expression in KIRC, HNSC, THCA, STAD and ESCA. The KIRC box plot shows higher APOE RNA expression in tumor versus normal tissue (log2 FC = +1.769, t-test p < 0.001).
This table shows molecular features associated with APOE in patient tissues and cancer cell lines. In patient samples, APOE shows the broadest associations at the RNA and protein expression levels, with COAD recurring as the lineage with the largest associated feature set. In cancer cell lines, APOE RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and BLOOD_Leukemia.
