Q-omics provides the consensus-scored EIF2B3 profile across patient tissues and cancer cell-line models. EIF2B3 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, EIF2B3 is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, EIF2B3 protein abundance shows 28,347 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, COAD, and LSCC as cancer lineages where EIF2B3 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 EIF2B3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF2B3 survival associations across molecular data types. EIF2B3 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) 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 EIF2B3 RNA expression–survival associations across cancer types. High EIF2B3 expression shows unfavorable associations in ACC, KICH, BLCA, LIHC, HNSC and LGG. 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 EIF2B3 RNA expression.
This table summarizes EIF2B3 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 10. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EIF2B3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF2B3 shows lower tumor expression in KICH and BRCA and higher tumor expression in COAD, LIHC, STAD and LUAD. The COAD box plot shows higher EIF2B3 RNA expression in tumor versus normal tissue (log2 FC = +0.967, t-test p < 0.001).
This table shows molecular features associated with EIF2B3 in patient tissues and cancer cell lines. In patient samples, EIF2B3 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, EIF2B3 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 OVARY and BLOOD_Lymphoma.
