Q-omics provides the consensus-scored EIF4G1 profile across patient tissues and cancer cell-line models. EIF4G1 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, EIF4G1 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, EIF4G1 protein abundance shows 27,155 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight BLCA, HNSC, and LSCC as cancer lineages where EIF4G1 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 EIF4G1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF4G1 survival associations across molecular data types. EIF4G1 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (14) 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 EIF4G1 RNA expression–survival associations across cancer types. High EIF4G1 expression shows unfavorable associations in BLCA, PAAD, LGG, LUAD, ACC and MESO. The BLCA 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 BLCA as the clearest survival context for EIF4G1 RNA expression.
This table summarizes EIF4G1 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 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for EIF4G1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF4G1 shows higher tumor expression in HNSC, COAD, LUSC, LIHC, LUAD and UCEC. The HNSC box plot shows higher EIF4G1 RNA expression in tumor versus normal tissue (log2 FC = +1.150, t-test p < 0.001).
This table shows molecular features associated with EIF4G1 in patient tissues and cancer cell lines. In patient samples, EIF4G1 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, EIF4G1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Lymphoma.
