Q-omics provides the consensus-scored FLYWCH2 profile across patient tissues and cancer cell-line models. FLYWCH2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in PAAD. Among the 18 cancer types available for tumor–normal comparison, FLYWCH2 is differentially expressed in 11, with the highest sampling consensus in KIRP. Additionally, FLYWCH2 protein abundance shows 19,989 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight PAAD, KIRP, and GBM as cancer lineages where FLYWCH2 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 FLYWCH2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FLYWCH2 survival associations across molecular data types. FLYWCH2 RNA expression shows survival associations in the most cancer types (26), followed by 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 FLYWCH2 RNA expression–survival associations across cancer types. High FLYWCH2 expression shows unfavorable associations in UCS, LIHC, ACC and ESCA, but favorable associations in PAAD and UVM. The PAAD 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 PAAD as the clearest survival context for FLYWCH2 RNA expression.
This table summarizes FLYWCH2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRP for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for FLYWCH2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FLYWCH2 shows lower tumor expression in KICH and THCA and higher tumor expression in KIRP, LIHC, BRCA and HNSC. The KIRP box plot shows higher FLYWCH2 RNA expression in tumor versus normal tissue (log2 FC = +1.034, t-test p < 0.001).
This table shows molecular features associated with FLYWCH2 in patient tissues and cancer cell lines. In patient samples, FLYWCH2 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, FLYWCH2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BREAST.
