Q-omics provides the consensus-scored FLT4 profile across patient tissues and cancer cell-line models. FLT4 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, FLT4 is differentially expressed in 12, with the highest sampling consensus in THCA. Additionally, FLT4 protein abundance shows 19,587 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, THCA, and GBM as cancer lineages where FLT4 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 FLT4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FLT4 survival associations across molecular data types. FLT4 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (7) 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 FLT4 RNA expression–survival associations across cancer types. High FLT4 expression shows unfavorable associations in KIRP, UVM, LUSC and MESO, but favorable associations in KIRC and LAML. 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 FLT4 RNA expression.
This table summarizes FLT4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 8. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for FLT4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FLT4 shows lower tumor expression in THCA, KIRP, UCEC and LUSC and higher tumor expression in HNSC and KIRC. The THCA box plot shows higher FLT4 RNA expression in normal versus tumor tissue (log2 FC = −1.841, t-test p < 0.001).
This table shows molecular features associated with FLT4 in patient tissues and cancer cell lines. In patient samples, FLT4 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, FLT4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and BONE.
