Q-omics provides the consensus-scored ZEB2 profile across patient tissues and cancer cell-line models. ZEB2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in LUAD. Among the 18 cancer types available for tumor–normal comparison, ZEB2 is differentially expressed in 13, with the highest sampling consensus in BLCA. Additionally, ZEB2 protein abundance shows 28,249 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight LUAD, BLCA, and LSCC as cancer lineages where ZEB2 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 ZEB2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZEB2 survival associations across molecular data types. ZEB2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (10) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ZEB2 RNA expression–survival associations across cancer types. High ZEB2 expression shows unfavorable associations in MESO and ACC, but favorable associations in LUAD, HNSC, UCS and KIRC. The LUAD Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify LUAD as the clearest survival context for ZEB2 RNA expression.
This table summarizes ZEB2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ZEB2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZEB2 shows lower tumor expression in BLCA, COAD, LUSC, UCEC and KICH and higher tumor expression in KIRC. The BLCA box plot shows higher ZEB2 RNA expression in normal versus tumor tissue (log2 FC = −1.881, t-test p < 0.001).
This table shows molecular features associated with ZEB2 in patient tissues and cancer cell lines. In patient samples, ZEB2 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, ZEB2 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 SKIN and BLOOD_Leukemia.
