Q-omics provides the consensus-scored EIF1B profile across patient tissues and cancer cell-line models. EIF1B expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, EIF1B is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, EIF1B RNA expression shows 19,074 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UCS, KIRC, and ACC as cancer lineages where EIF1B 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 EIF1B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF1B survival associations across molecular data types. EIF1B RNA expression shows survival associations in the most cancer types (24), followed by mutation status (1) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF1B RNA expression–survival associations across cancer types. High EIF1B expression shows unfavorable associations in LIHC, but favorable associations in UCS, UVM, KIRC, SKCM and COAD. The UCS 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 UCS as the clearest survival context for EIF1B RNA expression.
This table summarizes EIF1B tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EIF1B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF1B shows lower tumor expression in KIRC, LUAD, LUSC, KICH and UCEC and higher tumor expression in LIHC. The KIRC box plot shows higher EIF1B RNA expression in normal versus tumor tissue (log2 FC = −0.929, t-test p < 0.001).
This table shows molecular features associated with EIF1B in patient tissues and cancer cell lines. In patient samples, EIF1B shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, EIF1B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and UPPER_AERODIGESTIVE_TRACT.
