Q-omics provides the consensus-scored EIF1 profile across patient tissues and cancer cell-line models. EIF1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, EIF1 is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, EIF1 protein abundance shows 20,965 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KIRP, KIRC, and HNSC as cancer lineages where EIF1 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 EIF1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF1 survival associations across molecular data types. EIF1 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (3) 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 EIF1 RNA expression–survival associations across cancer types. High EIF1 expression shows unfavorable associations in KIRP, KICH, ACC, LIHC, BLCA and STAD. The KIRP 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 KIRP as the clearest survival context for EIF1 RNA expression.
This table summarizes EIF1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EIF1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF1 shows lower tumor expression in BLCA, LUAD and UCEC and higher tumor expression in KIRC, LIHC and HNSC. The KIRC box plot shows higher EIF1 RNA expression in tumor versus normal tissue (log2 FC = +0.610, t-test p < 0.001).
This table shows molecular features associated with EIF1 in patient tissues and cancer cell lines. In patient samples, EIF1 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, EIF1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and BLOOD_Lymphoma.
