Q-omics provides the consensus-scored VEGFB profile across patient tissues and cancer cell-line models. VEGFB expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, VEGFB is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, VEGFB RNA expression shows 18,357 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KIRC as cancer lineages where VEGFB 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 VEGFB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes VEGFB survival associations across molecular data types. VEGFB RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible VEGFB RNA expression–survival associations across cancer types. High VEGFB expression shows unfavorable associations in ACC, KICH, LGG and KIRP, but favorable associations in ESCA and LUAD. The ACC 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 ACC as the clearest survival context for VEGFB RNA expression.
This table summarizes VEGFB 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 1. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for VEGFB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. VEGFB shows lower tumor expression in THCA and BRCA and higher tumor expression in KIRC, KICH, KIRP and HNSC. The KIRC box plot shows higher VEGFB RNA expression in tumor versus normal tissue (log2 FC = +1.501, t-test p < 0.001).
This table shows molecular features associated with VEGFB in patient tissues and cancer cell lines. In patient samples, VEGFB 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, VEGFB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and SOFT_TISSUE.
