Q-omics provides the consensus-scored FGF13 profile across patient tissues and cancer cell-line models. FGF13 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in LGG. Among the 18 cancer types available for tumor–normal comparison, FGF13 is differentially expressed in 16, with the highest sampling consensus in BLCA. Additionally, FGF13 RNA expression shows 17,711 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LGG, BLCA, and GBM as cancer lineages where FGF13 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 FGF13 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGF13 survival associations across molecular data types. FGF13 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FGF13 RNA expression–survival associations across cancer types. High FGF13 expression shows unfavorable associations in LIHC, UVM and SCLC, but favorable associations in LGG, LAML and UCS. The LGG 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 LGG as the clearest survival context for FGF13 RNA expression.
This table summarizes FGF13 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 1. The strongest signals are observed in BLCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for FGF13. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGF13 shows lower tumor expression in BLCA, THCA, KIRC, COAD and BRCA and higher tumor expression in LIHC. The BLCA box plot shows higher FGF13 RNA expression in normal versus tumor tissue (log2 FC = −0.855, t-test p < 0.001).
This table shows molecular features associated with FGF13 in patient tissues and cancer cell lines. In patient samples, FGF13 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, FGF13 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in OVARY and LARGE_INTESTINE.
