Q-omics provides the consensus-scored UGP2 profile across patient tissues and cancer cell-line models. UGP2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, UGP2 is differentially expressed in 13, with the highest sampling consensus in COAD. Additionally, UGP2 protein abundance shows 21,285 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight LIHC, COAD, and HNSC as cancer lineages where UGP2 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 UGP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes UGP2 survival associations across molecular data types. UGP2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (6) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible UGP2 RNA expression–survival associations across cancer types. High UGP2 expression shows unfavorable associations in LGG, ACC, KICH and STAD, but favorable associations in LIHC and SKCM. The LIHC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify LIHC as the clearest survival context for UGP2 RNA expression.
This table summarizes UGP2 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 5. The strongest signals are observed in COAD for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for UGP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. UGP2 shows lower tumor expression in COAD, THCA, KICH, LIHC, READ and BRCA. The COAD box plot shows higher UGP2 RNA expression in normal versus tumor tissue (log2 FC = −2.384, t-test p < 0.001).
This table shows molecular features associated with UGP2 in patient tissues and cancer cell lines. In patient samples, UGP2 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, UGP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
