WRN RecQ like helicaseGenealiases: RECQ3 · RECQL2 · RECQL3
Q-omics provides the consensus-scored WRN profile across patient tissues and cancer cell-line models. WRN expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, WRN is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, WRN RNA expression shows 20,790 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight MESO, HNSC, and ACC as cancer lineages where WRN 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 WRN — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes WRN survival associations across molecular data types. WRN RNA expression shows survival associations in the most cancer types (24), followed by mutation status (8) 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 WRN RNA expression–survival associations across cancer types. High WRN expression shows unfavorable associations in MESO, KIRP and LIHC, but favorable associations in UCS, READ and KIRC. The MESO Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify MESO as the clearest survival context for WRN RNA expression.
This table summarizes WRN tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 3. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for WRN. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. WRN shows lower tumor expression in THCA and higher tumor expression in HNSC, KIRP, STAD, CHOL and LUAD. The HNSC box plot shows higher WRN RNA expression in tumor versus normal tissue (log2 FC = +0.652, t-test p < 0.001).
This table shows molecular features associated with WRN in patient tissues and cancer cell lines. In patient samples, WRN 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, WRN RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BLOOD_Leukemia.
