Q-omics provides the consensus-scored VEZF1 profile across patient tissues and cancer cell-line models. VEZF1 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, VEZF1 is differentially expressed in 10, with the highest sampling consensus in LIHC. Additionally, VEZF1 protein abundance shows 26,828 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, LIHC, and GBM as cancer lineages where VEZF1 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 VEZF1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes VEZF1 survival associations across molecular data types. VEZF1 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (6) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible VEZF1 RNA expression–survival associations across cancer types. High VEZF1 expression shows unfavorable associations in LIHC and ACC, but favorable associations in KIRC, COAD, LUAD and HNSC. The KIRC 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 KIRC as the clearest survival context for VEZF1 RNA expression.
This table summarizes VEZF1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 5. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for VEZF1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. VEZF1 shows lower tumor expression in THCA, LUSC, KICH and BLCA and higher tumor expression in LIHC and CHOL. The LIHC box plot shows higher VEZF1 RNA expression in tumor versus normal tissue (log2 FC = +0.995, t-test p < 0.001).
This table shows molecular features associated with VEZF1 in patient tissues and cancer cell lines. In patient samples, VEZF1 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, VEZF1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and BLOOD_Leukemia.
