Q-omics provides the consensus-scored FGF18 profile across patient tissues and cancer cell-line models. FGF18 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, FGF18 is differentially expressed in 15, with the highest sampling consensus in KICH. Additionally, FGF18 RNA expression shows 14,546 significant protein co-abundance associations, with the highest sampling consensus in BRCA. Together, these results highlight UVM, KICH, and BRCA as cancer lineages where FGF18 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 FGF18 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGF18 survival associations across molecular data types. FGF18 RNA expression shows survival associations in the most cancer types (25), 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 FGF18 RNA expression–survival associations across cancer types. High FGF18 expression shows unfavorable associations in UVM, KIRP, READ and KIRC, but favorable associations in UCS and COAD. The UVM 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 UVM as the clearest survival context for FGF18 RNA expression.
This table summarizes FGF18 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15. The strongest signals are observed in KICH for RNA.
This table ranks reproducible tumor–normal expression differences for FGF18. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGF18 shows lower tumor expression in KICH, LUAD, LUSC and KIRC and higher tumor expression in COAD and UCEC. The KICH box plot shows higher FGF18 RNA expression in normal versus tumor tissue (log2 FC = −2.018, t-test p < 0.001).
This table shows molecular features associated with FGF18 in patient tissues and cancer cell lines. In patient samples, FGF18 shows the broadest associations at the RNA and protein expression levels, with BRCA recurring as the lineage with the largest associated feature set. In cancer cell lines, FGF18 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and OVARY.
