Q-omics provides the consensus-scored ZDBF2 profile across patient tissues and cancer cell-line models. ZDBF2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, ZDBF2 is differentially expressed in 15, with the highest sampling consensus in KICH. Additionally, ZDBF2 RNA expression shows 19,862 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight UCS, KICH, and UVM as cancer lineages where ZDBF2 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 ZDBF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZDBF2 survival associations across molecular data types. ZDBF2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (13) 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 ZDBF2 RNA expression–survival associations across cancer types. High ZDBF2 expression shows unfavorable associations in DLBC, UCEC, KIRP and LUSC, but favorable associations in UCS and BRCA. The UCS 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 UCS as the clearest survival context for ZDBF2 RNA expression.
This table summarizes ZDBF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 1. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ZDBF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZDBF2 shows lower tumor expression in KICH, KIRC, COAD, BLCA and LIHC and higher tumor expression in HNSC. The KICH box plot shows higher ZDBF2 RNA expression in normal versus tumor tissue (log2 FC = −2.108, t-test p < 0.001).
This table shows molecular features associated with ZDBF2 in patient tissues and cancer cell lines. In patient samples, ZDBF2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, ZDBF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in CNS and UPPER_AERODIGESTIVE_TRACT.
