Q-omics provides the consensus-scored ZSWIM9 profile across patient tissues and cancer cell-line models. ZSWIM9 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, ZSWIM9 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, ZSWIM9 RNA expression shows 19,139 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight MESO, HNSC, and ACC as cancer lineages where ZSWIM9 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 ZSWIM9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ZSWIM9 survival associations across molecular data types. ZSWIM9 RNA expression shows survival associations in the most cancer types (24), followed by mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ZSWIM9 RNA expression–survival associations across cancer types. High ZSWIM9 expression shows unfavorable associations in MESO, ACC, LIHC and LGG, but favorable associations in KIRC and CHOL. 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 ZSWIM9 RNA expression.
This table summarizes ZSWIM9 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 1. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ZSWIM9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ZSWIM9 shows lower tumor expression in THCA, UCEC and BRCA and higher tumor expression in HNSC, LIHC and CHOL. The HNSC box plot shows higher ZSWIM9 RNA expression in tumor versus normal tissue (log2 FC = +0.512, t-test p < 0.001).
This table shows molecular features associated with ZSWIM9 in patient tissues and cancer cell lines. In patient samples, ZSWIM9 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, ZSWIM9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and LUNG_NSCLC_LUAD.
