Q-omics provides the consensus-scored ZNF767P profile across patient tissues and cancer cell-line models. ZNF767P expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in COAD. Among the 18 cancer types available for tumor–normal comparison, ZNF767P is differentially expressed in 8, with the highest sampling consensus in LIHC. Additionally, ZNF767P RNA expression shows 19,428 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight COAD, LIHC, and UVM as cancer lineages where ZNF767P 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 ZNF767P — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZNF767P survival associations across molecular data types. ZNF767P RNA expression shows survival associations in the most cancer types (26), followed by mutation status (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ZNF767P RNA expression–survival associations across cancer types. High ZNF767P expression shows unfavorable associations in COAD, ACC, KIRC and LIHC, but favorable associations in HNSC and SKCM. The COAD 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 COAD as the clearest survival context for ZNF767P RNA expression.
This table summarizes ZNF767P 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 LIHC for RNA.
This table ranks reproducible tumor–normal expression differences for ZNF767P. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZNF767P shows lower tumor expression in BRCA and higher tumor expression in LIHC, COAD, CHOL, READ and KIRC. The LIHC box plot shows higher ZNF767P RNA expression in tumor versus normal tissue (log2 FC = +1.219, t-test p < 0.001).
This table shows molecular features associated with ZNF767P in patient tissues and cancer cell lines. In patient samples, ZNF767P 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, ZNF767P RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and NCI60_ALL.
