Q-omics provides the consensus-scored FGF14 profile across patient tissues and cancer cell-line models. FGF14 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in STAD. Among the 18 cancer types available for tumor–normal comparison, FGF14 is differentially expressed in 13, with the highest sampling consensus in LUSC. Additionally, FGF14 RNA expression shows 17,902 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight STAD, LUSC, and PDAC as cancer lineages where FGF14 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 FGF14 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGF14 survival associations across molecular data types. FGF14 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FGF14 RNA expression–survival associations across cancer types. High FGF14 expression shows unfavorable associations in STAD and ESCA, but favorable associations in KIRC, SCLC, LUAD and LGG. The STAD Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .006). Together, the overview and detailed table identify STAD as the clearest survival context for FGF14 RNA expression.
This table summarizes FGF14 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 4. The strongest signals are observed in LUSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for FGF14. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGF14 shows lower tumor expression in LUSC, COAD, LUAD and UCEC and higher tumor expression in KICH and KIRC. The LUSC box plot shows higher FGF14 RNA expression in normal versus tumor tissue (log2 FC = −1.000, t-test p < 0.001).
This table shows molecular features associated with FGF14 in patient tissues and cancer cell lines. In patient samples, FGF14 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, FGF14 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and LARGE_INTESTINE.
