Q-omics provides the consensus-scored FSCB profile across patient tissues and cancer cell-line models. FSCB expression is associated with patient survival in 16 of 34 cancer types, with the highest sampling consensus in THCA. Additionally, FSCB mutation status shows 6,870 significant gene co-expression associations, with the highest sampling consensus in UCEC. Together, these results highlight THCA, and UCEC as cancer lineages where FSCB 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 FSCB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FSCB survival associations across molecular data types. FSCB RNA expression shows survival associations in the most cancer types (16), followed by mutation status (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FSCB RNA expression–survival associations across cancer types. High FSCB expression shows unfavorable associations in THCA, DLBC, KICH, LUSC, UCEC and UVM. The THCA 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 THCA as the clearest survival context for FSCB RNA expression.
This table shows molecular features associated with FSCB in patient tissues and cancer cell lines. In patient samples, FSCB shows the broadest associations at the RNA and protein expression levels, with UCEC recurring as the lineage with the largest associated feature set. In cancer cell lines, FSCB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and LARGE_INTESTINE.
