Q-omics provides the consensus-scored APOO profile across patient tissues and cancer cell-line models. APOO expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ESCA. Among the 18 cancer types available for tumor–normal comparison, APOO is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, APOO protein abundance shows 22,851 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ESCA, KIRC, and GBM as cancer lineages where APOO 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 APOO — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APOO survival associations across molecular data types. APOO RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) 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 APOO RNA expression–survival associations across cancer types. High APOO expression shows unfavorable associations in ESCA, UCS, LUAD, UVM and LIHC, but favorable associations in KIRP. The ESCA 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 ESCA as the clearest survival context for APOO RNA expression.
This table summarizes APOO tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for APOO. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APOO shows lower tumor expression in KIRC, THCA and KIRP and higher tumor expression in BLCA, LIHC and LUSC. The KIRC box plot shows higher APOO RNA expression in normal versus tumor tissue (log2 FC = −1.235, t-test p < 0.001).
This table shows molecular features associated with APOO in patient tissues and cancer cell lines. In patient samples, APOO shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, APOO 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 PANCREAS and BLOOD_Leukemia.
