Q-omics provides the consensus-scored EIF3C profile across patient tissues and cancer cell-line models. EIF3C expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, EIF3C is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, EIF3C protein abundance shows 19,846 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight BLCA, COAD, and PDAC as cancer lineages where EIF3C 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 EIF3C — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF3C survival associations across molecular data types. EIF3C RNA expression shows survival associations in the most cancer types (21), followed by mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EIF3C RNA expression–survival associations across cancer types. High EIF3C expression shows unfavorable associations in BLCA, MESO and CESC, but favorable associations in KIRC, UCS and BRCA. The BLCA Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify BLCA as the clearest survival context for EIF3C RNA expression.
This table summarizes EIF3C tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 6. The strongest signals are observed in COAD for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for EIF3C. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF3C shows higher tumor expression in COAD, LIHC, LUAD, STAD, LUSC and HNSC. The COAD box plot shows higher EIF3C RNA expression in tumor versus normal tissue (log2 FC = +0.753, t-test p < 0.001).
This table shows molecular features associated with EIF3C in patient tissues and cancer cell lines. In patient samples, EIF3C shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, EIF3C RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and OVARY.
