Q-omics provides the consensus-scored EPYC profile across patient tissues and cancer cell-line models. EPYC expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, EPYC is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, EPYC protein abundance shows 18,604 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight HNSC, and COAD as cancer lineages where EPYC 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 EPYC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EPYC survival associations across molecular data types. EPYC RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EPYC RNA expression–survival associations across cancer types. High EPYC expression shows unfavorable associations in KIRP, LGG, KIRC and UVM, but favorable associations in HNSC and BRCA. The HNSC 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 HNSC as the clearest survival context for EPYC RNA expression.
This table summarizes EPYC 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 COAD for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EPYC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EPYC shows higher tumor expression in COAD, HNSC, BRCA, LUAD, BLCA and STAD. The COAD box plot shows higher EPYC RNA expression in tumor versus normal tissue (log2 FC = +1.624, t-test p < 0.001).
This table shows molecular features associated with EPYC in patient tissues and cancer cell lines. In patient samples, EPYC shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, EPYC RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and UPPER_AERODIGESTIVE_TRACT.
