Q-omics provides the consensus-scored EFHB profile across patient tissues and cancer cell-line models. EFHB expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, EFHB is differentially expressed in 9, with the highest sampling consensus in KICH. Additionally, EFHB RNA expression shows 15,212 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight MESO, KICH, and TGCT as cancer lineages where EFHB 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 EFHB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EFHB survival associations across molecular data types. EFHB RNA expression shows survival associations in the most cancer types (24), followed by mutation status (9) 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 EFHB RNA expression–survival associations across cancer types. High EFHB expression shows unfavorable associations in BLCA and ACC, but favorable associations in MESO, READ, LGG and SKCM. 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 EFHB RNA expression.
This table summarizes EFHB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 2. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EFHB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EFHB shows lower tumor expression in KICH, THCA, LUSC, LUAD and BRCA and higher tumor expression in KIRP. The KICH box plot shows higher EFHB RNA expression in normal versus tumor tissue (log2 FC = −1.165, t-test p < 0.001).
This table shows molecular features associated with EFHB in patient tissues and cancer cell lines. In patient samples, EFHB shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, EFHB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and UPPER_AERODIGESTIVE_TRACT.
