Q-omics provides the consensus-scored UNC93B1 profile across patient tissues and cancer cell-line models. UNC93B1 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, UNC93B1 is differentially expressed in 15, with the highest sampling consensus in KIRC. Additionally, UNC93B1 protein abundance shows 31,955 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, KIRC, and GBM as cancer lineages where UNC93B1 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 UNC93B1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes UNC93B1 survival associations across molecular data types. UNC93B1 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (1) 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 UNC93B1 RNA expression–survival associations across cancer types. High UNC93B1 expression shows unfavorable associations in ACC, LGG, PAAD, LAML and BRCA, but favorable associations in BLCA. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify ACC as the clearest survival context for UNC93B1 RNA expression.
This table summarizes UNC93B1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 10. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for UNC93B1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. UNC93B1 shows higher tumor expression in KIRC, COAD, THCA, STAD, LIHC and KIRP. The KIRC box plot shows higher UNC93B1 RNA expression in tumor versus normal tissue (log2 FC = +0.939, t-test p < 0.001).
This table shows molecular features associated with UNC93B1 in patient tissues and cancer cell lines. In patient samples, UNC93B1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, UNC93B1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BLOOD_Lymphoma.
