Q-omics provides the consensus-scored EEF1G profile across patient tissues and cancer cell-line models. EEF1G expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, EEF1G is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, EEF1G protein abundance shows 22,126 significant protein co-abundance associations, with the highest sampling consensus in BRCA. Together, these results highlight ACC, KIRC, and BRCA as cancer lineages where EEF1G 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 EEF1G — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EEF1G survival associations across molecular data types. EEF1G RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EEF1G RNA expression–survival associations across cancer types. High EEF1G expression shows unfavorable associations in ACC, LIHC, UCEC, KICH and KIRP, but favorable associations in 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 EEF1G RNA expression.
This table summarizes EEF1G tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, 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 EEF1G. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EEF1G shows lower tumor expression in BRCA and higher tumor expression in KIRC, KIRP, LUSC, LIHC and CHOL. The KIRC box plot shows higher EEF1G RNA expression in tumor versus normal tissue (log2 FC = +0.225, t-test p < 0.001).
This table shows molecular features associated with EEF1G in patient tissues and cancer cell lines. In patient samples, EEF1G shows the broadest associations at the RNA and protein expression levels, with BRCA recurring as the lineage with the largest associated feature set. In cancer cell lines, EEF1G RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BONE and LARGE_INTESTINE.
