Q-omics provides the consensus-scored FGF8 profile across patient tissues and cancer cell-line models. FGF8 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, FGF8 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, FGF8 RNA expression shows 12,983 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight ACC, HNSC, and TGCT as cancer lineages where FGF8 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 FGF8 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGF8 survival associations across molecular data types. FGF8 RNA expression shows survival associations in the most cancer types (25), 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 FGF8 RNA expression–survival associations across cancer types. High FGF8 expression shows unfavorable associations in ACC, SCLC, BRCA and KIRC, but favorable associations in STAD and LGG. The ACC 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 ACC as the clearest survival context for FGF8 RNA expression.
This table summarizes FGF8 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 FGF8. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGF8 shows lower tumor expression in KIRC, BRCA, KICH and THCA and higher tumor expression in HNSC and COAD. The HNSC box plot shows higher FGF8 RNA expression in tumor versus normal tissue (log2 FC = +0.233, t-test p = .008).
This table shows molecular features associated with FGF8 in patient tissues and cancer cell lines. In patient samples, FGF8 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, FGF8 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BONE and SOFT_TISSUE.
