Q-omics provides the consensus-scored APOBEC2 profile across patient tissues and cancer cell-line models. APOBEC2 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, APOBEC2 is differentially expressed in 13, with the highest sampling consensus in BLCA. Additionally, APOBEC2 RNA expression shows 17,186 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UVM, BLCA, and TGCT as cancer lineages where APOBEC2 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 APOBEC2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APOBEC2 survival associations across molecular data types. APOBEC2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APOBEC2 RNA expression–survival associations across cancer types. High APOBEC2 expression shows unfavorable associations in ACC and CESC, but favorable associations in UVM, READ, SCLC and THCA. The UVM Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify UVM as the clearest survival context for APOBEC2 RNA expression.
This table summarizes APOBEC2 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 8. The strongest signals are observed in BLCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for APOBEC2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APOBEC2 shows lower tumor expression in BLCA, KICH, STAD, HNSC, LUSC and BRCA. The BLCA box plot shows higher APOBEC2 RNA expression in normal versus tumor tissue (log2 FC = −1.404, t-test p < 0.001).
This table shows molecular features associated with APOBEC2 in patient tissues and cancer cell lines. In patient samples, APOBEC2 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, APOBEC2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in LIVER and BLOOD_Leukemia.
