Q-omics provides the consensus-scored CYGB profile across patient tissues and cancer cell-line models. CYGB expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in UCEC. Among the 18 cancer types available for tumor–normal comparison, CYGB is differentially expressed in 16, with the highest sampling consensus in KIRC. Additionally, CYGB protein abundance shows 23,137 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UCEC, KIRC, and LSCC as cancer lineages where CYGB 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 CYGB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CYGB survival associations across molecular data types. CYGB RNA expression shows survival associations in the most cancer types (27), 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 CYGB RNA expression–survival associations across cancer types. High CYGB expression shows unfavorable associations in UVM, UCS and KIRP, but favorable associations in UCEC, HNSC and LIHC. The UCEC 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 UCEC as the clearest survival context for CYGB RNA expression.
This table summarizes CYGB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for CYGB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CYGB shows lower tumor expression in LUAD, COAD, BLCA and THCA and higher tumor expression in KIRC and HNSC. The KIRC box plot shows higher CYGB RNA expression in tumor versus normal tissue (log2 FC = +1.832, t-test p < 0.001).
This table shows molecular features associated with CYGB in patient tissues and cancer cell lines. In patient samples, CYGB shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, CYGB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and UPPER_AERODIGESTIVE_TRACT.
