Q-omics provides the consensus-scored FEZ2 profile across patient tissues and cancer cell-line models. FEZ2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, FEZ2 is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, FEZ2 RNA expression shows 19,863 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight LIHC, HNSC, and ACC as cancer lineages where FEZ2 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 FEZ2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FEZ2 survival associations across molecular data types. FEZ2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) 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 FEZ2 RNA expression–survival associations across cancer types. High FEZ2 expression shows unfavorable associations in LIHC, KIRP, STAD, LGG and SCLC, but favorable associations in KIRC. The LIHC 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 LIHC as the clearest survival context for FEZ2 RNA expression.
This table summarizes FEZ2 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 5. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for FEZ2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FEZ2 shows lower tumor expression in COAD, KICH, UCEC and BRCA and higher tumor expression in HNSC and LIHC. The HNSC box plot shows higher FEZ2 RNA expression in tumor versus normal tissue (log2 FC = +0.718, t-test p < 0.001).
This table shows molecular features associated with FEZ2 in patient tissues and cancer cell lines. In patient samples, FEZ2 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, FEZ2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and UPPER_AERODIGESTIVE_TRACT.
