Q-omics provides the consensus-scored VEGFD profile across patient tissues and cancer cell-line models. VEGFD expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in PAAD. Among the 18 cancer types available for tumor–normal comparison, VEGFD is differentially expressed in 15, with the highest sampling consensus in COAD. Additionally, VEGFD RNA expression shows 16,158 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight PAAD, COAD, and UVM as cancer lineages where VEGFD 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 VEGFD — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes VEGFD survival associations across molecular data types. VEGFD RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible VEGFD RNA expression–survival associations across cancer types. High VEGFD expression shows unfavorable associations in KICH, UVM, LGG and LUSC, but favorable associations in PAAD and LUAD. The PAAD 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 PAAD as the clearest survival context for VEGFD RNA expression.
This table summarizes VEGFD 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 2. The strongest signals are observed in COAD for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for VEGFD. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. VEGFD shows lower tumor expression in COAD, LUAD, BLCA, THCA and LUSC and higher tumor expression in LIHC. The COAD box plot shows higher VEGFD RNA expression in normal versus tumor tissue (log2 FC = −2.323, t-test p < 0.001).
This table shows molecular features associated with VEGFD in patient tissues and cancer cell lines. In patient samples, VEGFD shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, VEGFD RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BONE and SKIN.
