Q-omics provides the consensus-scored EIF2B1 profile across patient tissues and cancer cell-line models. EIF2B1 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, EIF2B1 is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, EIF2B1 protein abundance shows 23,526 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight LIHC, KIRC, and LUAD as cancer lineages where EIF2B1 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 EIF2B1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF2B1 survival associations across molecular data types. EIF2B1 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (3) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF2B1 RNA expression–survival associations across cancer types. High EIF2B1 expression shows unfavorable associations in LIHC, KICH, HNSC, MESO and LUAD, but favorable associations in UCS. The LIHC 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 LIHC as the clearest survival context for EIF2B1 RNA expression.
This table summarizes EIF2B1 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 8. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EIF2B1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF2B1 shows higher tumor expression in KIRC, HNSC, KIRP, COAD, LIHC and LUAD. The KIRC box plot shows higher EIF2B1 RNA expression in tumor versus normal tissue (log2 FC = +0.633, t-test p < 0.001).
This table shows molecular features associated with EIF2B1 in patient tissues and cancer cell lines. In patient samples, EIF2B1 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, EIF2B1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Myeloma.
