Q-omics provides the consensus-scored ZWINT profile across patient tissues and cancer cell-line models. ZWINT expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ZWINT is differentially expressed in 17, with the highest sampling consensus in HNSC. Additionally, ZWINT protein abundance shows 32,556 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight ACC, HNSC, and LUAD as cancer lineages where ZWINT 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 ZWINT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZWINT survival associations across molecular data types. ZWINT RNA expression shows survival associations in the most cancer types (29), followed by mutation status (5) and mass-spec protein abundance (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ZWINT RNA expression–survival associations across cancer types. High ZWINT expression shows unfavorable associations in ACC, MESO, LIHC, KICH, KIRP and LUAD. The ACC 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 ACC as the clearest survival context for ZWINT RNA expression.
This table summarizes ZWINT tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 8. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ZWINT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZWINT shows higher tumor expression in HNSC, BLCA, KIRP, COAD, LUSC and LUAD. The HNSC box plot shows higher ZWINT RNA expression in tumor versus normal tissue (log2 FC = +1.656, t-test p < 0.001).
This table shows molecular features associated with ZWINT in patient tissues and cancer cell lines. In patient samples, ZWINT shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, ZWINT RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and BLOOD_Leukemia.
