Q-omics provides the consensus-scored EIF3M profile across patient tissues and cancer cell-line models. EIF3M expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, EIF3M is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, EIF3M protein abundance shows 29,053 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight ACC, KIRC, and LUAD as cancer lineages where EIF3M 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 EIF3M — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EIF3M survival associations across molecular data types. EIF3M RNA expression shows survival associations in the most cancer types (23), followed by mutation status (7) 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 EIF3M RNA expression–survival associations across cancer types. High EIF3M expression shows unfavorable associations in ACC, KICH, LIHC, KIRP and PAAD, but favorable associations in KIRC. The ACC 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 ACC as the clearest survival context for EIF3M RNA expression.
This table summarizes EIF3M 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 7. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for EIF3M. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EIF3M shows lower tumor expression in THCA and higher tumor expression in KIRC, LIHC, COAD, STAD and HNSC. The KIRC box plot shows higher EIF3M RNA expression in tumor versus normal tissue (log2 FC = +0.446, t-test p < 0.001).
This table shows molecular features associated with EIF3M in patient tissues and cancer cell lines. In patient samples, EIF3M shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, EIF3M RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and SOFT_TISSUE.
