Q-omics provides the consensus-scored EIF5 profile across patient tissues and cancer cell-line models. EIF5 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, EIF5 is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, EIF5 RNA expression shows 19,812 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, HNSC, and ACC as cancer lineages where EIF5 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 EIF5 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF5 survival associations across molecular data types. EIF5 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (4) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF5 RNA expression–survival associations across cancer types. High EIF5 expression shows unfavorable associations in ACC, KICH, THCA and MESO, but favorable associations in KIRC and BRCA. The KIRC 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 KIRC as the clearest survival context for EIF5 RNA expression.
This table summarizes EIF5 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 8. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for EIF5. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF5 shows lower tumor expression in THCA, READ, LUAD and KIRP and higher tumor expression in HNSC and STAD. The HNSC box plot shows higher EIF5 RNA expression in tumor versus normal tissue (log2 FC = +0.569, t-test p < 0.001).
This table shows molecular features associated with EIF5 in patient tissues and cancer cell lines. In patient samples, EIF5 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, EIF5 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
