Q-omics provides the consensus-scored FZD2 profile across patient tissues and cancer cell-line models. FZD2 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, FZD2 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, FZD2 RNA expression shows 16,767 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRC, HNSC, and TGCT as cancer lineages where FZD2 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 FZD2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FZD2 survival associations across molecular data types. FZD2 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (3) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FZD2 RNA expression–survival associations across cancer types. High FZD2 expression shows unfavorable associations in KIRC, MESO, LGG, ACC and SARC, but favorable associations in SKCM. The KIRC 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 KIRC as the clearest survival context for FZD2 RNA expression.
This table summarizes FZD2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 1. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for FZD2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FZD2 shows lower tumor expression in KIRC and higher tumor expression in HNSC, BLCA, BRCA, STAD and LIHC. The HNSC box plot shows higher FZD2 RNA expression in tumor versus normal tissue (log2 FC = +2.563, t-test p < 0.001).
This table shows molecular features associated with FZD2 in patient tissues and cancer cell lines. In patient samples, FZD2 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, FZD2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
