Q-omics provides the consensus-scored ZWILCH profile across patient tissues and cancer cell-line models. ZWILCH expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, ZWILCH is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, ZWILCH protein abundance shows 29,057 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight MESO, HNSC, and LSCC as cancer lineages where ZWILCH 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 ZWILCH — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZWILCH survival associations across molecular data types. ZWILCH RNA expression shows survival associations in the most cancer types (22), followed by mutation status (7) 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 ZWILCH RNA expression–survival associations across cancer types. High ZWILCH expression shows unfavorable associations in MESO, ACC, PAAD, KIRP, LUAD and LGG. The MESO 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 MESO as the clearest survival context for ZWILCH RNA expression.
This table summarizes ZWILCH tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ZWILCH. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZWILCH shows higher tumor expression in HNSC, BLCA, STAD, LUAD, LIHC and COAD. The HNSC box plot shows higher ZWILCH RNA expression in tumor versus normal tissue (log2 FC = +1.338, t-test p < 0.001).
This table shows molecular features associated with ZWILCH in patient tissues and cancer cell lines. In patient samples, ZWILCH 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, ZWILCH RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in CNS and BLOOD_Leukemia.
