Q-omics provides the consensus-scored TRAPPC13 profile across patient tissues and cancer cell-line models. TRAPPC13 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, TRAPPC13 is differentially expressed in 8, with the highest sampling consensus in THCA. Additionally, TRAPPC13 RNA expression shows 19,824 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, THCA, and ACC as cancer lineages where TRAPPC13 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 TRAPPC13 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TRAPPC13 survival associations across molecular data types. TRAPPC13 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (2) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible TRAPPC13 RNA expression–survival associations across cancer types. High TRAPPC13 expression shows unfavorable associations in UVM and ESCA, but favorable associations in KIRC, LUAD, UCS and COAD. 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 TRAPPC13 RNA expression.
This table summarizes TRAPPC13 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 6. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for TRAPPC13. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. TRAPPC13 shows lower tumor expression in THCA, LUSC and KIRC and higher tumor expression in LIHC, BRCA and CHOL. The THCA box plot shows higher TRAPPC13 RNA expression in normal versus tumor tissue (log2 FC = −0.569, t-test p < 0.001).
This table shows molecular features associated with TRAPPC13 in patient tissues and cancer cell lines. In patient samples, TRAPPC13 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, TRAPPC13 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 BLOOD_Lymphoma and BLOOD_Leukemia.
