Q-omics provides the consensus-scored CRYGA profile across patient tissues and cancer cell-line models. CRYGA expression is associated with patient survival in 11 of 34 cancer types, with the highest sampling consensus in LIHC. Additionally, CRYGA RNA expression shows 6,677 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight LIHC, and STAD as cancer lineages where CRYGA 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 CRYGA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CRYGA survival associations across molecular data types. CRYGA RNA expression shows survival associations in the most cancer types (11), followed by mutation status (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CRYGA RNA expression–survival associations across cancer types. High CRYGA expression shows unfavorable associations in LIHC, SKCM, CESC, COAD and ACC, but favorable associations in LGG. The LIHC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify LIHC as the clearest survival context for CRYGA RNA expression.
This table shows molecular features associated with CRYGA in patient tissues and cancer cell lines. In patient samples, CRYGA shows the broadest associations at the RNA and protein expression levels, with STAD recurring as the lineage with the largest associated feature set. In cancer cell lines, CRYGA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BONE and BREAST.
