Q-omics provides the consensus-scored YAP1 profile across patient tissues and cancer cell-line models. YAP1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, YAP1 is differentially expressed in 12, with the highest sampling consensus in LIHC. Additionally, YAP1 protein abundance shows 25,785 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight ACC, LIHC, and LUAD as cancer lineages where YAP1 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 YAP1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes YAP1 survival associations across molecular data types. YAP1 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible YAP1 RNA expression–survival associations across cancer types. High YAP1 expression shows unfavorable associations in ACC, PAAD, LGG and BLCA, but favorable associations in UCS and KIRC. 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 YAP1 RNA expression.
This table summarizes YAP1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 9. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for YAP1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. YAP1 shows lower tumor expression in BLCA, KICH, UCEC and BRCA and higher tumor expression in LIHC and COAD. The LIHC box plot shows higher YAP1 RNA expression in tumor versus normal tissue (log2 FC = +0.853, t-test p < 0.001).
This table shows molecular features associated with YAP1 in patient tissues and cancer cell lines. In patient samples, YAP1 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, YAP1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BONE and LARGE_INTESTINE.
