Q-omics provides the consensus-scored EIF4E profile across patient tissues and cancer cell-line models. EIF4E expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, EIF4E is differentially expressed in 9, with the highest sampling consensus in THCA. Additionally, EIF4E RNA expression shows 20,179 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight LIHC, THCA, and UVM as cancer lineages where EIF4E 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 EIF4E — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF4E survival associations across molecular data types. EIF4E RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) 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 EIF4E RNA expression–survival associations across cancer types. High EIF4E expression shows unfavorable associations in LIHC, UVM, HNSC and SCLC, but favorable associations in KIRC and UCEC. The LIHC 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 LIHC as the clearest survival context for EIF4E RNA expression.
This table summarizes EIF4E 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 4. The strongest signals are observed in THCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for EIF4E. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF4E shows lower tumor expression in THCA and KIRC and higher tumor expression in LIHC, BRCA, CHOL and LUAD. The THCA box plot shows higher EIF4E RNA expression in normal versus tumor tissue (log2 FC = −0.600, t-test p < 0.001).
This table shows molecular features associated with EIF4E in patient tissues and cancer cell lines. In patient samples, EIF4E shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, EIF4E RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BONE and SOFT_TISSUE.
