Q-omics provides the consensus-scored CYBB profile across patient tissues and cancer cell-line models. CYBB 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, CYBB is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, CYBB protein abundance shows 27,333 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SKCM, KIRC, and LSCC as cancer lineages where CYBB 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 CYBB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CYBB survival associations across molecular data types. CYBB RNA expression shows survival associations in the most cancer types (26), followed by mutation status (7) 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 CYBB RNA expression–survival associations across cancer types. High CYBB expression shows unfavorable associations in LGG, but favorable associations in SKCM, HNSC, CESC, UCEC and LUAD. 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 CYBB RNA expression.
This table summarizes CYBB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for CYBB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CYBB shows lower tumor expression in LUAD and LUSC and higher tumor expression in KIRC, KIRP, HNSC and STAD. The KIRC box plot shows higher CYBB RNA expression in tumor versus normal tissue (log2 FC = +2.271, t-test p < 0.001).
This table shows molecular features associated with CYBB in patient tissues and cancer cell lines. In patient samples, CYBB 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, CYBB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUSC, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Lymphoma.
