Q-omics provides the consensus-scored ENSA profile across patient tissues and cancer cell-line models. ENSA expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, ENSA is differentially expressed in 9, with the highest sampling consensus in KICH. Additionally, ENSA RNA expression shows 19,538 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, KICH, and ACC as cancer lineages where ENSA 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 ENSA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ENSA survival associations across molecular data types. ENSA RNA expression shows survival associations in the most cancer types (23), followed by mutation status (2) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ENSA RNA expression–survival associations across cancer types. High ENSA expression shows unfavorable associations in UVM, ACC, KIRP, KICH, ESCA and LIHC. The UVM 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 UVM as the clearest survival context for ENSA RNA expression.
This table summarizes ENSA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 6. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ENSA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ENSA shows lower tumor expression in KICH and THCA and higher tumor expression in LIHC, BRCA, LUAD and CHOL. The KICH box plot shows higher ENSA RNA expression in normal versus tumor tissue (log2 FC = −1.217, t-test p < 0.001).
This table shows molecular features associated with ENSA in patient tissues and cancer cell lines. In patient samples, ENSA 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, ENSA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BONE and UPPER_AERODIGESTIVE_TRACT.
