Q-omics provides the consensus-scored FBXW2 profile across patient tissues and cancer cell-line models. FBXW2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, FBXW2 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, FBXW2 RNA expression shows 21,088 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where FBXW2 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 FBXW2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FBXW2 survival associations across molecular data types. FBXW2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FBXW2 RNA expression–survival associations across cancer types. High FBXW2 expression shows unfavorable associations in ACC, LGG and UVM, but favorable associations in KIRC, HNSC and THYM. The ACC 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 ACC as the clearest survival context for FBXW2 RNA expression.
This table summarizes FBXW2 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 6. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for FBXW2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FBXW2 shows lower tumor expression in LUAD and THCA and higher tumor expression in HNSC, LIHC, CHOL and STAD. The HNSC box plot shows higher FBXW2 RNA expression in tumor versus normal tissue (log2 FC = +0.794, t-test p < 0.001).
This table shows molecular features associated with FBXW2 in patient tissues and cancer cell lines. In patient samples, FBXW2 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, FBXW2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SOFT_TISSUE.
