Q-omics provides the consensus-scored EBF3 profile across patient tissues and cancer cell-line models. EBF3 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, EBF3 is differentially expressed in 10, with the highest sampling consensus in THCA. Additionally, EBF3 RNA expression shows 17,689 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight KIRP, THCA, and THYM as cancer lineages where EBF3 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 EBF3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EBF3 survival associations across molecular data types. EBF3 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (7) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EBF3 RNA expression–survival associations across cancer types. High EBF3 expression shows unfavorable associations in KIRP and COAD, but favorable associations in KIRC, HNSC, KICH and LIHC. The KIRP 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 KIRP as the clearest survival context for EBF3 RNA expression.
This table summarizes EBF3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 3. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EBF3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EBF3 shows lower tumor expression in THCA, BLCA and BRCA and higher tumor expression in KIRC, LIHC and HNSC. The THCA box plot shows higher EBF3 RNA expression in normal versus tumor tissue (log2 FC = −1.086, t-test p < 0.001).
This table shows molecular features associated with EBF3 in patient tissues and cancer cell lines. In patient samples, EBF3 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, EBF3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BONE.
