Q-omics provides the consensus-scored EZH1 profile across patient tissues and cancer cell-line models. EZH1 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in BRCA. Among the 18 cancer types available for tumor–normal comparison, EZH1 is differentially expressed in 12, with the highest sampling consensus in LIHC. Additionally, EZH1 RNA expression shows 20,828 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight BRCA, LIHC, and UVM as cancer lineages where EZH1 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 EZH1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EZH1 survival associations across molecular data types. EZH1 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (6) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EZH1 RNA expression–survival associations across cancer types. High EZH1 expression shows unfavorable associations in LIHC and OV, but favorable associations in BRCA, SKCM, HNSC and KIRC. The BRCA 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 BRCA as the clearest survival context for EZH1 RNA expression.
This table summarizes EZH1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EZH1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EZH1 shows lower tumor expression in BLCA, LUAD, KICH, LUSC and UCEC and higher tumor expression in LIHC. The LIHC box plot shows higher EZH1 RNA expression in tumor versus normal tissue (log2 FC = +1.289, t-test p < 0.001).
This table shows molecular features associated with EZH1 in patient tissues and cancer cell lines. In patient samples, EZH1 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, EZH1 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 LARGE_INTESTINE.
