Q-omics provides the consensus-scored EPHX2 profile across patient tissues and cancer cell-line models. EPHX2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, EPHX2 is differentially expressed in 14, with the highest sampling consensus in BLCA. Additionally, EPHX2 protein abundance shows 18,436 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight KIRC, BLCA, and LUAD as cancer lineages where EPHX2 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 EPHX2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EPHX2 survival associations across molecular data types. EPHX2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EPHX2 RNA expression–survival associations across cancer types. High EPHX2 expression shows favorable associations in KIRC, UVM, KIRP, HNSC, CESC and MESO. The KIRC 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 KIRC as the clearest survival context for EPHX2 RNA expression.
This table summarizes EPHX2 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 9. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EPHX2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EPHX2 shows lower tumor expression in BLCA, COAD, HNSC, KIRP, LUAD and KICH. The BLCA box plot shows higher EPHX2 RNA expression in normal versus tumor tissue (log2 FC = −2.019, t-test p < 0.001).
This table shows molecular features associated with EPHX2 in patient tissues and cancer cell lines. In patient samples, EPHX2 shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, EPHX2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and UPPER_AERODIGESTIVE_TRACT.
