Q-omics provides the consensus-scored CRYZ profile across patient tissues and cancer cell-line models. CRYZ expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, CRYZ is differentially expressed in 9, with the highest sampling consensus in KICH. Additionally, CRYZ protein abundance shows 23,461 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight KIRC, KICH, and LUAD as cancer lineages where CRYZ 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 CRYZ — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CRYZ survival associations across molecular data types. CRYZ RNA expression shows survival associations in the most cancer types (20), followed by mutation status (3) 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 CRYZ RNA expression–survival associations across cancer types. High CRYZ expression shows unfavorable associations in LGG, BLCA, PAAD and GBM, but favorable associations in KIRC and BRCA. The KIRC 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 KIRC as the clearest survival context for CRYZ RNA expression.
This table summarizes CRYZ tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 8. The strongest signals are observed in KICH for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for CRYZ. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CRYZ shows lower tumor expression in KICH, LUSC, KIRP, THCA and PRAD and higher tumor expression in COAD. The KICH box plot shows higher CRYZ RNA expression in normal versus tumor tissue (log2 FC = −4.144, t-test p < 0.001).
This table shows molecular features associated with CRYZ in patient tissues and cancer cell lines. In patient samples, CRYZ shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, CRYZ RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in OVARY and UPPER_AERODIGESTIVE_TRACT.
