Q-omics provides the consensus-scored EIF3E profile across patient tissues and cancer cell-line models. EIF3E expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, EIF3E is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, EIF3E protein abundance shows 37,153 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRP, KIRC, and GBM as cancer lineages where EIF3E 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 EIF3E — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF3E survival associations across molecular data types. EIF3E RNA expression shows survival associations in the most cancer types (27), followed by mutation status (5) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF3E RNA expression–survival associations across cancer types. High EIF3E expression shows unfavorable associations in KIRP, HNSC, UVM, ACC, KICH and CESC. 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 EIF3E RNA expression.
This table summarizes EIF3E tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 13. The strongest signals are observed in KIRC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for EIF3E. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF3E shows higher tumor expression in KIRC, LIHC, COAD, HNSC, CHOL and LUAD. The KIRC box plot shows higher EIF3E RNA expression in tumor versus normal tissue (log2 FC = +0.681, t-test p < 0.001).
This table shows molecular features associated with EIF3E in patient tissues and cancer cell lines. In patient samples, EIF3E shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, EIF3E RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
