Q-omics provides the consensus-scored CYLD profile across patient tissues and cancer cell-line models. CYLD expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, CYLD is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, CYLD protein abundance shows 28,256 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight SKCM, KIRC, and GBM as cancer lineages where CYLD 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 CYLD — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CYLD survival associations across molecular data types. CYLD RNA expression shows survival associations in the most cancer types (26), followed by mutation status (7) 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 CYLD RNA expression–survival associations across cancer types. High CYLD expression shows unfavorable associations in LUSC and LGG, but favorable associations in SKCM, LUAD, KIRC and CESC. The SKCM 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 SKCM as the clearest survival context for CYLD RNA expression.
This table summarizes CYLD tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for CYLD. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CYLD shows lower tumor expression in THCA, COAD, UCEC and LUSC and higher tumor expression in KIRC and HNSC. The KIRC box plot shows higher CYLD RNA expression in tumor versus normal tissue (log2 FC = +0.604, t-test p < 0.001).
This table shows molecular features associated with CYLD in patient tissues and cancer cell lines. In patient samples, CYLD shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, CYLD 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 LUNG_SCLC and CNS.
