Q-omics provides the consensus-scored FGF6 profile across patient tissues and cancer cell-line models. FGF6 expression is associated with patient survival in 17 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, FGF6 is differentially expressed in 5, with the highest sampling consensus in BLCA. Additionally, FGF6 RNA expression shows 7,351 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight MESO, BLCA, and TGCT as cancer lineages where FGF6 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 FGF6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGF6 survival associations across molecular data types. FGF6 RNA expression shows survival associations in the most cancer types (17), followed by mutation status (8) 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 FGF6 RNA expression–survival associations across cancer types. High FGF6 expression shows unfavorable associations in MESO, ACC, THCA and THYM, but favorable associations in UCS and SARC. The MESO 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 MESO as the clearest survival context for FGF6 RNA expression.
This table summarizes FGF6 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5, while mass-spec protein shows differences in 1. The strongest signals are observed in BLCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for FGF6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGF6 shows lower tumor expression in BLCA, HNSC, PRAD and READ and higher tumor expression in UCEC. The BLCA box plot shows higher FGF6 RNA expression in normal versus tumor tissue (log2 FC = −0.260, t-test p = .018).
This table shows molecular features associated with FGF6 in patient tissues and cancer cell lines. In patient samples, FGF6 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, FGF6 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 URINARY_TRACT and BLOOD_Leukemia.
