Q-omics provides the consensus-scored EIF3D profile across patient tissues and cancer cell-line models. EIF3D expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, EIF3D is differentially expressed in 15, with the highest sampling consensus in COAD. Additionally, EIF3D protein abundance shows 30,543 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, COAD, and LSCC as cancer lineages where EIF3D 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 EIF3D — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF3D survival associations across molecular data types. EIF3D RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF3D RNA expression–survival associations across cancer types. High EIF3D expression shows unfavorable associations in ACC, LIHC, KICH, PAAD and HNSC, 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 EIF3D RNA expression.
This table summarizes EIF3D tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 9. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for EIF3D. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF3D shows higher tumor expression in COAD, KIRC, HNSC, LIHC, LUSC and LUAD. The COAD box plot shows higher EIF3D RNA expression in tumor versus normal tissue (log2 FC = +0.969, t-test p < 0.001).
This table shows molecular features associated with EIF3D in patient tissues and cancer cell lines. In patient samples, EIF3D shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, EIF3D RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and CNS.
