EBNA1 binding protein 2Genealiases: EBP2 · NOBP · P40
Q-omics provides the consensus-scored EBNA1BP2 profile across patient tissues and cancer cell-line models. EBNA1BP2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, EBNA1BP2 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, EBNA1BP2 protein abundance shows 37,579 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, HNSC, and LSCC as cancer lineages where EBNA1BP2 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 EBNA1BP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EBNA1BP2 survival associations across molecular data types. EBNA1BP2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (5) and mass-spec protein abundance (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EBNA1BP2 RNA expression–survival associations across cancer types. High EBNA1BP2 expression shows unfavorable associations in ACC, MESO, LGG, BLCA, LIHC and KIRP. The ACC 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 ACC as the clearest survival context for EBNA1BP2 RNA expression.
This table summarizes EBNA1BP2 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 11. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EBNA1BP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EBNA1BP2 shows lower tumor expression in KICH and higher tumor expression in HNSC, BLCA, LIHC, COAD and LUAD. The HNSC box plot shows higher EBNA1BP2 RNA expression in tumor versus normal tissue (log2 FC = +1.028, t-test p < 0.001).
This table shows molecular features associated with EBNA1BP2 in patient tissues and cancer cell lines. In patient samples, EBNA1BP2 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, EBNA1BP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Lymphoma.
