Q-omics provides the consensus-scored FXR2 profile across patient tissues and cancer cell-line models. FXR2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, FXR2 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, FXR2 RNA expression shows 19,444 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight SCLC, HNSC, and ACC as cancer lineages where FXR2 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 FXR2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FXR2 survival associations across molecular data types. FXR2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (7) 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 FXR2 RNA expression–survival associations across cancer types. High FXR2 expression shows unfavorable associations in LUAD, LUSC and KICH, but favorable associations in SCLC, PAAD and LGG. The SCLC 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 SCLC as the clearest survival context for FXR2 RNA expression.
This table summarizes FXR2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 4. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for FXR2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FXR2 shows lower tumor expression in KICH and LUAD and higher tumor expression in HNSC, LIHC, CHOL and KIRP. The HNSC box plot shows higher FXR2 RNA expression in tumor versus normal tissue (log2 FC = +0.504, t-test p < 0.001).
This table shows molecular features associated with FXR2 in patient tissues and cancer cell lines. In patient samples, FXR2 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, FXR2 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 URINARY_TRACT and BLOOD_Leukemia.
