Q-omics provides the consensus-scored EPHA1 profile across patient tissues and cancer cell-line models. EPHA1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, EPHA1 is differentially expressed in 15, with the highest sampling consensus in KIRC. Additionally, EPHA1 RNA expression shows 17,818 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight SCLC, KIRC, and UVM as cancer lineages where EPHA1 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 EPHA1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EPHA1 survival associations across molecular data types. EPHA1 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (7) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EPHA1 RNA expression–survival associations across cancer types. High EPHA1 expression shows unfavorable associations in LGG, CESC and COAD, but favorable associations in SCLC, MESO and KIRP. The SCLC 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 SCLC as the clearest survival context for EPHA1 RNA expression.
This table summarizes EPHA1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EPHA1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EPHA1 shows lower tumor expression in KIRC and KICH and higher tumor expression in COAD, LUSC, BLCA and LUAD. The KIRC box plot shows higher EPHA1 RNA expression in normal versus tumor tissue (log2 FC = −2.108, t-test p < 0.001).
This table shows molecular features associated with EPHA1 in patient tissues and cancer cell lines. In patient samples, EPHA1 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, EPHA1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in OESOPHAGUS and LUNG_NSCLC_LUAD.
