Q-omics provides the consensus-scored FGF22 profile across patient tissues and cancer cell-line models. FGF22 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, FGF22 is differentially expressed in 10, with the highest sampling consensus in BLCA. Additionally, FGF22 RNA expression shows 14,148 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight HNSC, BLCA, and UVM as cancer lineages where FGF22 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 FGF22 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGF22 survival associations across molecular data types. FGF22 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (1) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FGF22 RNA expression–survival associations across cancer types. High FGF22 expression shows unfavorable associations in COAD, MESO and OV, but favorable associations in HNSC, PAAD and UCEC. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify HNSC as the clearest survival context for FGF22 RNA expression.
This table summarizes FGF22 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 2. The strongest signals are observed in LIHC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for FGF22. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGF22 shows lower tumor expression in LUAD, KIRC and KICH and higher tumor expression in BLCA, LIHC and BRCA. The BLCA box plot shows higher FGF22 RNA expression in tumor versus normal tissue (log2 FC = +0.278, t-test p = .008).
This table shows molecular features associated with FGF22 in patient tissues and cancer cell lines. In patient samples, FGF22 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, FGF22 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in OVARY and LARGE_INTESTINE.
