Q-omics provides the consensus-scored EIF2A profile across patient tissues and cancer cell-line models. EIF2A expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, EIF2A is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, EIF2A protein abundance shows 24,546 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, and LSCC as cancer lineages where EIF2A 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 EIF2A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF2A survival associations across molecular data types. EIF2A RNA expression shows survival associations in the most cancer types (25), followed by mutation status (5) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF2A RNA expression–survival associations across cancer types. High EIF2A expression shows unfavorable associations in KIRP, ACC, LIHC and PAAD, but favorable associations in KIRC and SKCM. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for EIF2A RNA expression.
This table summarizes EIF2A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for EIF2A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF2A shows lower tumor expression in THCA and higher tumor expression in KIRC, LIHC, COAD, HNSC and CHOL. The KIRC box plot shows higher EIF2A RNA expression in tumor versus normal tissue (log2 FC = +0.701, t-test p < 0.001).
This table shows molecular features associated with EIF2A in patient tissues and cancer cell lines. In patient samples, EIF2A 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, EIF2A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in OVARY and CNS.
