Q-omics provides the consensus-scored EIF3F profile across patient tissues and cancer cell-line models. EIF3F expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, EIF3F is differentially expressed in 10, with the highest sampling consensus in LIHC. Additionally, EIF3F protein abundance shows 29,906 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, LIHC, and GBM as cancer lineages where EIF3F 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 EIF3F — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF3F survival associations across molecular data types. EIF3F RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) 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 EIF3F RNA expression–survival associations across cancer types. High EIF3F expression shows unfavorable associations in ACC, LIHC and KICH, but favorable associations in LGG, BRCA and MESO. 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 EIF3F RNA expression.
This table summarizes EIF3F 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 5. The strongest signals are observed in LIHC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for EIF3F. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF3F shows lower tumor expression in BRCA and BLCA and higher tumor expression in LIHC, KIRC, COAD and CHOL. The LIHC box plot shows higher EIF3F RNA expression in tumor versus normal tissue (log2 FC = +0.955, t-test p < 0.001).
This table shows molecular features associated with EIF3F in patient tissues and cancer cell lines. In patient samples, EIF3F 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, EIF3F RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
