Q-omics provides the consensus-scored EEF1A2 profile across patient tissues and cancer cell-line models. EEF1A2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, EEF1A2 is differentially expressed in 15, with the highest sampling consensus in LIHC. Additionally, EEF1A2 protein abundance shows 23,221 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight UVM, LIHC, and HNSC as cancer lineages where EEF1A2 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 EEF1A2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EEF1A2 survival associations across molecular data types. EEF1A2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EEF1A2 RNA expression–survival associations across cancer types. High EEF1A2 expression shows unfavorable associations in UVM, STAD, UCEC and KIRC, but favorable associations in KICH and LIHC. The UVM 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 UVM as the clearest survival context for EEF1A2 RNA expression.
This table summarizes EEF1A2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 5. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EEF1A2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EEF1A2 shows lower tumor expression in HNSC, STAD and COAD and higher tumor expression in LIHC, LUAD and BRCA. The LIHC box plot shows higher EEF1A2 RNA expression in tumor versus normal tissue (log2 FC = +4.748, t-test p < 0.001).
This table shows molecular features associated with EEF1A2 in patient tissues and cancer cell lines. In patient samples, EEF1A2 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, EEF1A2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and LARGE_INTESTINE.
