Q-omics provides the consensus-scored EIF1AY profile across patient tissues and cancer cell-line models. EIF1AY expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, EIF1AY is differentially expressed in 9, with the highest sampling consensus in KICH. Additionally, EIF1AY RNA expression shows 5,704 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight MESO, KICH, and HNSC as cancer lineages where EIF1AY 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 EIF1AY — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF1AY survival associations across molecular data types. EIF1AY RNA expression shows survival associations in the most cancer types (26), followed by 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 EIF1AY RNA expression–survival associations across cancer types. High EIF1AY expression shows unfavorable associations in THCA, SCLC and LIHC, but favorable associations in MESO, UVM and HNSC. The MESO 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 MESO as the clearest survival context for EIF1AY RNA expression.
This table summarizes EIF1AY tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRP for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EIF1AY. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF1AY shows lower tumor expression in KICH, KIRP, HNSC, BLCA and READ and higher tumor expression in CHOL. The KICH box plot shows higher EIF1AY RNA expression in normal versus tumor tissue (log2 FC = −3.269, t-test p < 0.001).
This table shows molecular features associated with EIF1AY in patient tissues and cancer cell lines. In patient samples, EIF1AY 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, EIF1AY 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 CNS and LUNG_NSCLC_LUAD.
