Q-omics provides the consensus-scored EIF3K profile across patient tissues and cancer cell-line models. EIF3K expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, EIF3K is differentially expressed in 9, with the highest sampling consensus in LIHC. Additionally, EIF3K protein abundance shows 22,990 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight ACC, LIHC, and LUAD as cancer lineages where EIF3K 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 EIF3K — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF3K survival associations across molecular data types. EIF3K RNA expression shows survival associations in the most cancer types (21), followed by mutation status (4) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF3K RNA expression–survival associations across cancer types. High EIF3K expression shows unfavorable associations in ACC, UCEC, OV, LGG, LUAD and LIHC. 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 EIF3K RNA expression.
This table summarizes EIF3K 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 6. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EIF3K. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF3K shows lower tumor expression in BRCA and higher tumor expression in LIHC, CHOL, LUSC, ESCA and COAD. The LIHC box plot shows higher EIF3K RNA expression in tumor versus normal tissue (log2 FC = +1.407, t-test p < 0.001).
This table shows molecular features associated with EIF3K in patient tissues and cancer cell lines. In patient samples, EIF3K shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, EIF3K RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BONE.
