Q-omics provides the consensus-scored CYBA profile across patient tissues and cancer cell-line models. CYBA expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, CYBA is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, CYBA protein abundance shows 28,070 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UCS, KIRC, and LSCC as cancer lineages where CYBA 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 CYBA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CYBA survival associations across molecular data types. CYBA RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CYBA RNA expression–survival associations across cancer types. High CYBA expression shows unfavorable associations in UCS, LUSC, KIRC and LGG, but favorable associations in UCEC and KIRP. The UCS Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .004). Together, the overview and detailed table identify UCS as the clearest survival context for CYBA RNA expression.
This table summarizes CYBA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 9. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for CYBA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CYBA shows lower tumor expression in KICH and higher tumor expression in KIRC, KIRP, STAD, BLCA and UCEC. The KIRC box plot shows higher CYBA RNA expression in tumor versus normal tissue (log2 FC = +1.422, t-test p < 0.001).
This table shows molecular features associated with CYBA in patient tissues and cancer cell lines. In patient samples, CYBA 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, CYBA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
