Q-omics provides the consensus-scored FGF4 profile across patient tissues and cancer cell-line models. FGF4 expression is associated with patient survival in 17 of 34 cancer types, with the highest sampling consensus in THYM. Among the 18 cancer types available for tumor–normal comparison, FGF4 is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, FGF4 RNA expression shows 6,960 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight THYM, KIRC, and TGCT as cancer lineages where FGF4 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 FGF4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGF4 survival associations across molecular data types. FGF4 RNA expression shows survival associations in the most cancer types (17), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FGF4 RNA expression–survival associations across cancer types. High FGF4 expression shows unfavorable associations in THYM, CHOL, UVM and LUAD, but favorable associations in CESC and READ. The THYM 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 THYM as the clearest survival context for FGF4 RNA expression.
This table summarizes FGF4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 1. The strongest signals are observed in KIRC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for FGF4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGF4 shows lower tumor expression in KIRC and KICH and higher tumor expression in STAD, READ, THCA and UCEC. The KIRC box plot shows higher FGF4 RNA expression in normal versus tumor tissue (log2 FC = −0.027, t-test p < 0.001).
This table shows molecular features associated with FGF4 in patient tissues and cancer cell lines. In patient samples, FGF4 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, FGF4 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 LUNG_NSCLC_LUSC and PANCREAS.
