Q-omics provides the consensus-scored EIF4H profile across patient tissues and cancer cell-line models. EIF4H expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, EIF4H is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, EIF4H RNA expression shows 19,646 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where EIF4H 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 EIF4H — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF4H survival associations across molecular data types. EIF4H RNA expression shows survival associations in the most cancer types (26), followed by mutation status (6) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF4H RNA expression–survival associations across cancer types. High EIF4H expression shows unfavorable associations in ACC, LUAD, PAAD, KICH and LIHC, but favorable associations in KIRC. 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 EIF4H RNA expression.
This table summarizes EIF4H tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, 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 EIF4H. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF4H shows lower tumor expression in THCA and higher tumor expression in HNSC, KIRC, LIHC, CHOL and LUSC. The HNSC box plot shows higher EIF4H RNA expression in tumor versus normal tissue (log2 FC = +0.661, t-test p < 0.001).
This table shows molecular features associated with EIF4H in patient tissues and cancer cell lines. In patient samples, EIF4H shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, EIF4H 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 UPPER_AERODIGESTIVE_TRACT and CNS.
