Q-omics provides the consensus-scored FGB profile across patient tissues and cancer cell-line models. FGB expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in LUAD. Among the 18 cancer types available for tumor–normal comparison, FGB is differentially expressed in 7, with the highest sampling consensus in KICH. Additionally, FGB protein abundance shows 21,263 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LUAD, KICH, and GBM as cancer lineages where FGB 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 FGB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGB survival associations across molecular data types. FGB RNA expression shows survival associations in the most cancer types (21), followed by mutation status (9) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FGB RNA expression–survival associations across cancer types. High FGB expression shows unfavorable associations in LUAD, STAD and DLBC, but favorable associations in LIHC, LGG and KIRC. The LUAD Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify LUAD as the clearest survival context for FGB RNA expression.
This table summarizes FGB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 4. The strongest signals are observed in KICH for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for FGB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGB shows lower tumor expression in KICH, LIHC and CHOL and higher tumor expression in LUAD, COAD and KIRC. The KICH box plot shows higher FGB RNA expression in normal versus tumor tissue (log2 FC = −3.197, t-test p < 0.001).
This table shows molecular features associated with FGB in patient tissues and cancer cell lines. In patient samples, FGB 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, FGB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in LIVER and CNS.
