Q-omics provides the consensus-scored EPHB2 profile across patient tissues and cancer cell-line models. EPHB2 expression is associated with patient survival in 31 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, EPHB2 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, EPHB2 RNA expression shows 18,011 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, HNSC, and UVM as cancer lineages where EPHB2 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 EPHB2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EPHB2 survival associations across molecular data types. EPHB2 RNA expression shows survival associations in the most cancer types (31), 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 EPHB2 RNA expression–survival associations across cancer types. High EPHB2 expression shows unfavorable associations in KIRC, UVM, UCEC and LGG, but favorable associations in SKCM and SCLC. The KIRC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for EPHB2 RNA expression.
This table summarizes EPHB2 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 5. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for EPHB2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EPHB2 shows lower tumor expression in KIRC and higher tumor expression in HNSC, STAD, LUAD, BLCA and LIHC. The HNSC box plot shows higher EPHB2 RNA expression in tumor versus normal tissue (log2 FC = +1.957, t-test p < 0.001).
This table shows molecular features associated with EPHB2 in patient tissues and cancer cell lines. In patient samples, EPHB2 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, EPHB2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in SKIN and CNS.
