Q-omics provides the consensus-scored EEF1B2 profile across patient tissues and cancer cell-line models. EEF1B2 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, EEF1B2 is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, EEF1B2 protein abundance shows 23,643 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, KIRC, and PDAC as cancer lineages where EEF1B2 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 EEF1B2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EEF1B2 survival associations across molecular data types. EEF1B2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (2) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EEF1B2 RNA expression–survival associations across cancer types. High EEF1B2 expression shows unfavorable associations in ACC, KIRP, LIHC and KICH, but favorable associations in LGG and KIRC. 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 EEF1B2 RNA expression.
This table summarizes EEF1B2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for EEF1B2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EEF1B2 shows lower tumor expression in BLCA and HNSC and higher tumor expression in KIRC, LIHC, COAD and KIRP. The KIRC box plot shows higher EEF1B2 RNA expression in tumor versus normal tissue (log2 FC = +1.033, t-test p < 0.001).
This table shows molecular features associated with EEF1B2 in patient tissues and cancer cell lines. In patient samples, EEF1B2 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, EEF1B2 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 LUNG_SCLC and UPPER_AERODIGESTIVE_TRACT.
