Q-omics provides the consensus-scored EIF3G profile across patient tissues and cancer cell-line models. EIF3G expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, EIF3G is differentially expressed in 15, with the highest sampling consensus in COAD. Additionally, EIF3G protein abundance shows 29,876 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight SCLC, COAD, and GBM as cancer lineages where EIF3G 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 EIF3G — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF3G survival associations across molecular data types. EIF3G RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF3G RNA expression–survival associations across cancer types. High EIF3G expression shows unfavorable associations in ACC, THCA and KICH, but favorable associations in SCLC, OV and UVM. The SCLC 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 SCLC as the clearest survival context for EIF3G RNA expression.
This table summarizes EIF3G 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 9. The strongest signals are observed in COAD for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for EIF3G. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF3G shows higher tumor expression in COAD, KIRP, KIRC, LIHC, HNSC and THCA. The COAD box plot shows higher EIF3G RNA expression in tumor versus normal tissue (log2 FC = +1.005, t-test p < 0.001).
This table shows molecular features associated with EIF3G in patient tissues and cancer cell lines. In patient samples, EIF3G shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, EIF3G 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 LUNG_NSCLC_LUAD and LARGE_INTESTINE.
