Q-omics provides the consensus-scored APOF profile across patient tissues and cancer cell-line models. APOF 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, APOF is differentially expressed in 9, with the highest sampling consensus in LIHC. Additionally, APOF protein abundance shows 18,116 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, LIHC, and GBM as cancer lineages where APOF 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 APOF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APOF survival associations across molecular data types. APOF RNA expression shows survival associations in the most cancer types (24), followed by mutation status (1) and 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 APOF RNA expression–survival associations across cancer types. High APOF expression shows unfavorable associations in UVM, but favorable associations in LIHC, SCLC, PAAD, BRCA and COAD. The UVM 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 UVM as the clearest survival context for APOF RNA expression.
This table summarizes APOF 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 5. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for APOF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APOF shows lower tumor expression in LIHC, KICH, KIRC and CHOL and higher tumor expression in BRCA and LUSC. The LIHC box plot shows higher APOF RNA expression in normal versus tumor tissue (log2 FC = −4.398, t-test p < 0.001).
This table shows molecular features associated with APOF in patient tissues and cancer cell lines. In patient samples, APOF 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, APOF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and UPPER_AERODIGESTIVE_TRACT.
