Q-omics provides the consensus-scored FSHR profile across patient tissues and cancer cell-line models. FSHR expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, FSHR is differentially expressed in 9, with the highest sampling consensus in KICH. Additionally, FSHR RNA expression shows 10,096 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UVM, KICH, and TGCT as cancer lineages where FSHR 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 FSHR — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FSHR survival associations across molecular data types. FSHR RNA expression shows survival associations in the most cancer types (21), followed by mutation status (7) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FSHR RNA expression–survival associations across cancer types. High FSHR expression shows unfavorable associations in UVM, READ and ACC, but favorable associations in KIRP, UCS and CHOL. The UVM 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 UVM as the clearest survival context for FSHR RNA expression.
This table summarizes FSHR tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for FSHR. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FSHR shows lower tumor expression in KICH, KIRC, BLCA, LUAD and ESCA and higher tumor expression in KIRP. The KICH box plot shows higher FSHR RNA expression in normal versus tumor tissue (log2 FC = −0.224, t-test p < 0.001).
This table shows molecular features associated with FSHR in patient tissues and cancer cell lines. In patient samples, FSHR shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, FSHR RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and LARGE_INTESTINE.
