Q-omics provides the consensus-scored EIF3L profile across patient tissues and cancer cell-line models. EIF3L expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, EIF3L is differentially expressed in 10, with the highest sampling consensus in BLCA. Additionally, EIF3L protein abundance shows 32,692 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, BLCA, and GBM as cancer lineages where EIF3L 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 EIF3L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF3L survival associations across molecular data types. EIF3L RNA expression shows survival associations in the most cancer types (26), followed by mutation status (9) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF3L RNA expression–survival associations across cancer types. High EIF3L expression shows unfavorable associations in ACC, but favorable associations in KIRC, UCS, BRCA, LGG and GBM. 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 EIF3L RNA expression.
This table summarizes EIF3L tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 9. The strongest signals are observed in THCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for EIF3L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF3L shows lower tumor expression in BLCA, THCA, UCEC and BRCA and higher tumor expression in CHOL and LIHC. The BLCA box plot shows higher EIF3L RNA expression in normal versus tumor tissue (log2 FC = −0.842, t-test p = .001).
This table shows molecular features associated with EIF3L in patient tissues and cancer cell lines. In patient samples, EIF3L 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, EIF3L RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and SOFT_TISSUE.
