Q-omics provides the consensus-scored FGF2 profile across patient tissues and cancer cell-line models. FGF2 expression is associated with patient survival in 15 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, FGF2 is differentially expressed in 12, with the highest sampling consensus in BLCA. Additionally, FGF2 protein abundance shows 25,614 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight SKCM, BLCA, and LUAD as cancer lineages where FGF2 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 FGF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGF2 survival associations across molecular data types. FGF2 RNA expression shows survival associations in the most cancer types (15), followed by mutation status (2) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FGF2 RNA expression–survival associations across cancer types. High FGF2 expression shows unfavorable associations in BLCA, MESO, DLBC and PAAD, but favorable associations in SKCM and BRCA. The SKCM 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 SKCM as the clearest survival context for FGF2 RNA expression.
This table summarizes FGF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in BLCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for FGF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGF2 shows lower tumor expression in BLCA, LUSC, LUAD, COAD, KICH and UCEC. The BLCA box plot shows higher FGF2 RNA expression in normal versus tumor tissue (log2 FC = −2.614, t-test p < 0.001).
This table shows molecular features associated with FGF2 in patient tissues and cancer cell lines. In patient samples, FGF2 shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, FGF2 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 BLOOD_Leukemia and BONE.
