Q-omics provides the consensus-scored YWHAE profile across patient tissues and cancer cell-line models. YWHAE expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, YWHAE is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, YWHAE RNA expression shows 19,094 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, HNSC, and ACC as cancer lineages where YWHAE 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 YWHAE — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes YWHAE survival associations across molecular data types. YWHAE RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) 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 YWHAE RNA expression–survival associations across cancer types. High YWHAE expression shows unfavorable associations in UVM, LUAD, ACC and HNSC, but favorable associations in UCEC and KIRC. The UVM Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify UVM as the clearest survival context for YWHAE RNA expression.
This table summarizes YWHAE 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 6. The strongest signals are observed in HNSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for YWHAE. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. YWHAE shows lower tumor expression in KICH and higher tumor expression in HNSC, BLCA, STAD, COAD and LIHC. The HNSC box plot shows higher YWHAE RNA expression in tumor versus normal tissue (log2 FC = +0.662, t-test p < 0.001).
This table shows molecular features associated with YWHAE in patient tissues and cancer cell lines. In patient samples, YWHAE shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, YWHAE 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 BLOOD_Leukemia and PANCREAS.
