Q-omics provides the consensus-scored FGR profile across patient tissues and cancer cell-line models. FGR expression is associated with patient survival in 30 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, FGR is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, FGR protein abundance shows 26,191 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight HNSC, KIRC, and PDAC as cancer lineages where FGR 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 FGR — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGR survival associations across molecular data types. FGR RNA expression shows survival associations in the most cancer types (30), followed by mutation status (2) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FGR RNA expression–survival associations across cancer types. High FGR expression shows unfavorable associations in UVM, LGG and LAML, but favorable associations in HNSC, SKCM 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 FGR RNA expression.
This table summarizes FGR 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 6. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for FGR. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGR shows lower tumor expression in LUAD, KICH, LUSC, BRCA and UCEC and higher tumor expression in KIRC. The KIRC box plot shows higher FGR RNA expression in tumor versus normal tissue (log2 FC = +1.544, t-test p < 0.001).
This table shows molecular features associated with FGR in patient tissues and cancer cell lines. In patient samples, FGR 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, FGR RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and OVARY.
