Q-omics provides the consensus-scored EIF4A3 profile across patient tissues and cancer cell-line models. EIF4A3 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, EIF4A3 is differentially expressed in 15, with the highest sampling consensus in COAD. Additionally, EIF4A3 protein abundance shows 31,244 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, COAD, and LSCC as cancer lineages where EIF4A3 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 EIF4A3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF4A3 survival associations across molecular data types. EIF4A3 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (3) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF4A3 RNA expression–survival associations across cancer types. High EIF4A3 expression shows unfavorable associations in ACC, KIRP, HNSC, BLCA, UVM and LIHC. 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 EIF4A3 RNA expression.
This table summarizes EIF4A3 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 6. The strongest signals are observed in BLCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for EIF4A3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF4A3 shows higher tumor expression in COAD, BLCA, HNSC, LIHC, KIRP and LUAD. The COAD box plot shows higher EIF4A3 RNA expression in tumor versus normal tissue (log2 FC = +1.027, t-test p < 0.001).
This table shows molecular features associated with EIF4A3 in patient tissues and cancer cell lines. In patient samples, EIF4A3 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, EIF4A3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
