Q-omics provides the consensus-scored FGF9 profile across patient tissues and cancer cell-line models. FGF9 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, FGF9 is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, FGF9 RNA expression shows 17,826 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight MESO, KIRC, and UVM as cancer lineages where FGF9 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 FGF9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGF9 survival associations across molecular data types. FGF9 RNA expression shows survival associations in the most cancer types (20), 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 FGF9 RNA expression–survival associations across cancer types. High FGF9 expression shows unfavorable associations in UVM, ACC, LUSC and LIHC, but favorable associations in MESO and PAAD. The MESO Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify MESO as the clearest survival context for FGF9 RNA expression.
This table summarizes FGF9 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 LSCC for protein.
This table ranks reproducible tumor–normal expression differences for FGF9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGF9 shows lower tumor expression in KIRC, KIRP, BLCA, LUSC, COAD and BRCA. The KIRC box plot shows higher FGF9 RNA expression in normal versus tumor tissue (log2 FC = −4.067, t-test p < 0.001).
This table shows molecular features associated with FGF9 in patient tissues and cancer cell lines. In patient samples, FGF9 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, FGF9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
