Q-omics provides the consensus-scored FEZ1 profile across patient tissues and cancer cell-line models. FEZ1 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, FEZ1 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, FEZ1 RNA expression shows 23,764 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, HNSC, and GBM as cancer lineages where FEZ1 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 FEZ1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FEZ1 survival associations across molecular data types. FEZ1 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (3) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FEZ1 RNA expression–survival associations across cancer types. High FEZ1 expression shows unfavorable associations in UVM, MESO, KIRP, STAD and BRCA, but favorable associations in SKCM. 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 FEZ1 RNA expression.
This table summarizes FEZ1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 2. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for FEZ1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FEZ1 shows lower tumor expression in LUAD, KICH, THCA, COAD and BLCA and higher tumor expression in HNSC. The HNSC box plot shows higher FEZ1 RNA expression in tumor versus normal tissue (log2 FC = +2.616, t-test p < 0.001).
This table shows molecular features associated with FEZ1 in patient tissues and cancer cell lines. In patient samples, FEZ1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, FEZ1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and SOFT_TISSUE.
