Q-omics provides the consensus-scored APEX2 profile across patient tissues and cancer cell-line models. APEX2 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in OV. Among the 18 cancer types available for tumor–normal comparison, APEX2 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, APEX2 RNA expression shows 17,592 significant gene co-expression associations, with the highest sampling consensus in KICH. Together, these results highlight OV, HNSC, and KICH as cancer lineages where APEX2 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 APEX2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APEX2 survival associations across molecular data types. APEX2 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (6) 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 APEX2 RNA expression–survival associations across cancer types. High APEX2 expression shows unfavorable associations in LIHC, KIRP, LGG and KICH, but favorable associations in OV and SCLC. The OV 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 OV as the clearest survival context for APEX2 RNA expression.
This table summarizes APEX2 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 3. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for APEX2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APEX2 shows higher tumor expression in HNSC, KIRC, KIRP, COAD, LIHC and LUSC. The HNSC box plot shows higher APEX2 RNA expression in tumor versus normal tissue (log2 FC = +1.108, t-test p < 0.001).
This table shows molecular features associated with APEX2 in patient tissues and cancer cell lines. In patient samples, APEX2 shows the broadest associations at the RNA and protein expression levels, with KICH recurring as the lineage with the largest associated feature set. In cancer cell lines, APEX2 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 SKIN and UPPER_AERODIGESTIVE_TRACT.
