Q-omics provides the consensus-scored ZBBX profile across patient tissues and cancer cell-line models. ZBBX expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, ZBBX is differentially expressed in 8, with the highest sampling consensus in KICH. Additionally, ZBBX RNA expression shows 13,593 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight BLCA, KICH, and TGCT as cancer lineages where ZBBX 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 ZBBX — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZBBX survival associations across molecular data types. ZBBX RNA expression shows survival associations in the most cancer types (26), followed by mutation status (11) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ZBBX RNA expression–survival associations across cancer types. High ZBBX expression shows unfavorable associations in BLCA, KICH, COAD and LIHC, but favorable associations in KIRP and HNSC. The BLCA 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 BLCA as the clearest survival context for ZBBX RNA expression.
This table summarizes ZBBX tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8. The strongest signals are observed in KICH for RNA.
This table ranks reproducible tumor–normal expression differences for ZBBX. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZBBX shows lower tumor expression in KICH, LUSC, LUAD and COAD and higher tumor expression in KIRP and BRCA. The KICH box plot shows higher ZBBX RNA expression in normal versus tumor tissue (log2 FC = −0.535, t-test p < 0.001).
This table shows molecular features associated with ZBBX in patient tissues and cancer cell lines. In patient samples, ZBBX shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, ZBBX RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and LARGE_INTESTINE.
