Q-omics provides the consensus-scored FOXL2 profile across patient tissues and cancer cell-line models. FOXL2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, FOXL2 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, FOXL2 RNA expression shows 12,493 significant gene co-expression associations, with the highest sampling consensus in ESCA. Together, these results highlight KIRP, HNSC, and ESCA as cancer lineages where FOXL2 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 FOXL2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FOXL2 survival associations across molecular data types. FOXL2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FOXL2 RNA expression–survival associations across cancer types. High FOXL2 expression shows unfavorable associations in KIRP, HNSC, MESO, UVM and LUAD, but favorable associations in UCEC. The KIRP 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 KIRP as the clearest survival context for FOXL2 RNA expression.
This table summarizes FOXL2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for FOXL2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FOXL2 shows lower tumor expression in UCEC and higher tumor expression in HNSC, BLCA, LUSC, LUAD and PRAD. The HNSC box plot shows higher FOXL2 RNA expression in tumor versus normal tissue (log2 FC = +1.551, t-test p < 0.001).
This table shows molecular features associated with FOXL2 in patient tissues and cancer cell lines. In patient samples, FOXL2 shows the broadest associations at the RNA and protein expression levels, with ESCA recurring as the lineage with the largest associated feature set. In cancer cell lines, FOXL2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in CNS and OESOPHAGUS.
