Q-omics provides the consensus-scored EPHX1 profile across patient tissues and cancer cell-line models. EPHX1 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, EPHX1 is differentially expressed in 14, with the highest sampling consensus in KICH. Additionally, EPHX1 protein abundance shows 19,824 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight MESO, KICH, and LUAD as cancer lineages where EPHX1 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 EPHX1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EPHX1 survival associations across molecular data types. EPHX1 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (6) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EPHX1 RNA expression–survival associations across cancer types. High EPHX1 expression shows unfavorable associations in KICH, UCS and BLCA, but favorable associations in MESO, KIRC and LUSC. The MESO 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 MESO as the clearest survival context for EPHX1 RNA expression.
This table summarizes EPHX1 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 6. The strongest signals are observed in THCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for EPHX1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EPHX1 shows lower tumor expression in KICH, THCA, COAD, LUSC and CHOL and higher tumor expression in LIHC. The KICH box plot shows higher EPHX1 RNA expression in normal versus tumor tissue (log2 FC = −1.907, t-test p < 0.001).
This table shows molecular features associated with EPHX1 in patient tissues and cancer cell lines. In patient samples, EPHX1 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, EPHX1 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 UPPER_AERODIGESTIVE_TRACT and SOFT_TISSUE.
