Q-omics provides the consensus-scored UMPS profile across patient tissues and cancer cell-line models. UMPS expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, UMPS is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, UMPS protein abundance shows 32,119 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, HNSC, and PDAC as cancer lineages where UMPS 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 UMPS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes UMPS survival associations across molecular data types. UMPS RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible UMPS RNA expression–survival associations across cancer types. High UMPS expression shows unfavorable associations in ACC, LIHC, MESO, KICH and UCEC, but favorable associations in LUSC. 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 UMPS RNA expression.
This table summarizes UMPS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 10. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for UMPS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. UMPS shows lower tumor expression in THCA and higher tumor expression in HNSC, COAD, BLCA, LIHC and LUSC. The HNSC box plot shows higher UMPS RNA expression in tumor versus normal tissue (log2 FC = +1.373, t-test p < 0.001).
This table shows molecular features associated with UMPS in patient tissues and cancer cell lines. In patient samples, UMPS shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, UMPS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and BLOOD_Lymphoma.
