Q-omics provides the consensus-scored APOBEC3F profile across patient tissues and cancer cell-line models. APOBEC3F expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, APOBEC3F is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, APOBEC3F RNA expression shows 19,013 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight SKCM, KIRC, and UVM as cancer lineages where APOBEC3F 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 APOBEC3F — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APOBEC3F survival associations across molecular data types. APOBEC3F RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APOBEC3F RNA expression–survival associations across cancer types. High APOBEC3F expression shows unfavorable associations in UVM, LGG and ACC, but favorable associations in SKCM, CESC and ESCA. The SKCM 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 SKCM as the clearest survival context for APOBEC3F RNA expression.
This table summarizes APOBEC3F tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for APOBEC3F. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APOBEC3F shows lower tumor expression in KICH and higher tumor expression in KIRC, HNSC, KIRP, LUSC and STAD. The KIRC box plot shows higher APOBEC3F RNA expression in tumor versus normal tissue (log2 FC = +1.108, t-test p < 0.001).
This table shows molecular features associated with APOBEC3F in patient tissues and cancer cell lines. In patient samples, APOBEC3F 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, APOBEC3F RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and BLOOD_Leukemia.
