Q-omics provides the consensus-scored VPS53 profile across patient tissues and cancer cell-line models. VPS53 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, VPS53 is differentially expressed in 13, with the highest sampling consensus in LIHC. Additionally, VPS53 RNA expression shows 20,030 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, LIHC, and ACC as cancer lineages where VPS53 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 VPS53 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes VPS53 survival associations across molecular data types. VPS53 RNA expression shows survival associations in the most cancer types (25), 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 VPS53 RNA expression–survival associations across cancer types. High VPS53 expression shows unfavorable associations in UVM, MESO, SKCM and ACC, but favorable associations in SCLC and PAAD. 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 VPS53 RNA expression.
This table summarizes VPS53 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 3. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for VPS53. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. VPS53 shows lower tumor expression in KIRC, BRCA and KICH and higher tumor expression in LIHC, CHOL and STAD. The LIHC box plot shows higher VPS53 RNA expression in tumor versus normal tissue (log2 FC = +0.843, t-test p < 0.001).
This table shows molecular features associated with VPS53 in patient tissues and cancer cell lines. In patient samples, VPS53 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, VPS53 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BLOOD_Leukemia.
