Q-omics provides the consensus-scored EFTUD2 profile across patient tissues and cancer cell-line models. EFTUD2 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, EFTUD2 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, EFTUD2 protein abundance shows 34,120 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight MESO, HNSC, and LSCC as cancer lineages where EFTUD2 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 EFTUD2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EFTUD2 survival associations across molecular data types. EFTUD2 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EFTUD2 RNA expression–survival associations across cancer types. High EFTUD2 expression shows unfavorable associations in MESO, ACC, LIHC, KIRP, PAAD and BLCA. The MESO 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 MESO as the clearest survival context for EFTUD2 RNA expression.
This table summarizes EFTUD2 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 KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for EFTUD2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EFTUD2 shows higher tumor expression in HNSC, KIRC, KIRP, BLCA, COAD and LIHC. The HNSC box plot shows higher EFTUD2 RNA expression in tumor versus normal tissue (log2 FC = +1.049, t-test p < 0.001).
This table shows molecular features associated with EFTUD2 in patient tissues and cancer cell lines. In patient samples, EFTUD2 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, EFTUD2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in OVARY and UPPER_AERODIGESTIVE_TRACT.
