Q-omics provides the consensus-scored EHF profile across patient tissues and cancer cell-line models. EHF expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, EHF is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, EHF protein abundance shows 26,689 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight KIRP, KIRC, and LUAD as cancer lineages where EHF 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 EHF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EHF survival associations across molecular data types. EHF RNA expression shows survival associations in the most cancer types (28), 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 EHF RNA expression–survival associations across cancer types. High EHF expression shows unfavorable associations in KIRP and PAAD, but favorable associations in DLBC, READ, HNSC and UCEC. 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 EHF RNA expression.
This table summarizes EHF tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EHF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EHF shows lower tumor expression in KIRC, HNSC, KICH and KIRP and higher tumor expression in BLCA and CHOL. The KIRC box plot shows higher EHF RNA expression in normal versus tumor tissue (log2 FC = −4.714, t-test p < 0.001).
This table shows molecular features associated with EHF in patient tissues and cancer cell lines. In patient samples, EHF shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, EHF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in LIVER and OVARY.
