Q-omics provides the consensus-scored FCSK profile across patient tissues and cancer cell-line models. FCSK expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, FCSK is differentially expressed in 10, with the highest sampling consensus in KICH. Additionally, FCSK RNA expression shows 18,966 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight KIRP, KICH, and THYM as cancer lineages where FCSK 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 FCSK — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FCSK survival associations across molecular data types. FCSK RNA expression shows survival associations in the most cancer types (24), followed by mutation status (10) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FCSK RNA expression–survival associations across cancer types. High FCSK expression shows unfavorable associations in LGG and LIHC, but favorable associations in KIRP, SCLC, HNSC and KICH. The KIRP 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 KIRP as the clearest survival context for FCSK RNA expression.
This table summarizes FCSK tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 6. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for FCSK. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FCSK shows lower tumor expression in KICH and higher tumor expression in COAD, LIHC, STAD, CHOL and BLCA. The KICH box plot shows higher FCSK RNA expression in normal versus tumor tissue (log2 FC = −1.345, t-test p < 0.001).
This table shows molecular features associated with FCSK in patient tissues and cancer cell lines. In patient samples, FCSK shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, FCSK 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 UPPER_AERODIGESTIVE_TRACT and LARGE_INTESTINE.
