Q-omics provides the consensus-scored FGF16 profile across patient tissues and cancer cell-line models. FGF16 expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in ESCA. Among the 18 cancer types available for tumor–normal comparison, FGF16 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, FGF16 RNA expression shows 11,969 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight ESCA, KIRC, and THYM as cancer lineages where FGF16 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 FGF16 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGF16 survival associations across molecular data types. FGF16 RNA expression shows survival associations in the most cancer types (19), followed by mutation status (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FGF16 RNA expression–survival associations across cancer types. High FGF16 expression shows unfavorable associations in READ, THCA, KIRC and UCEC, but favorable associations in ESCA and UCS. The ESCA 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 ESCA as the clearest survival context for FGF16 RNA expression.
This table summarizes FGF16 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for FGF16. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGF16 shows lower tumor expression in KIRC, BLCA, COAD, KIRP, BRCA and THCA. The KIRC box plot shows higher FGF16 RNA expression in normal versus tumor tissue (log2 FC = −0.104, t-test p < 0.001).
This table shows molecular features associated with FGF16 in patient tissues and cancer cell lines. In patient samples, FGF16 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, FGF16 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and PANCREAS.
