Q-omics provides the consensus-scored FLG2 profile across patient tissues and cancer cell-line models. FLG2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in READ. Among the 18 cancer types available for tumor–normal comparison, FLG2 is differentially expressed in 5, with the highest sampling consensus in KICH. Additionally, FLG2 RNA expression shows 13,780 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight READ, KICH, and UVM as cancer lineages where FLG2 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 FLG2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FLG2 survival associations across molecular data types. FLG2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (12) 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 FLG2 RNA expression–survival associations across cancer types. High FLG2 expression shows unfavorable associations in READ, SKCM, OV, CHOL and THCA, but favorable associations in UCS. The READ 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 READ as the clearest survival context for FLG2 RNA expression.
This table summarizes FLG2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 5, while mass-spec protein shows differences in 1. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for FLG2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FLG2 shows lower tumor expression in STAD and HNSC and higher tumor expression in KICH, LUSC and KIRP. The KICH box plot shows higher FLG2 RNA expression in tumor versus normal tissue (log2 FC = +0.049, t-test p = .024).
This table shows molecular features associated with FLG2 in patient tissues and cancer cell lines. In patient samples, FLG2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, FLG2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUSC, while CRISPR and shRNA rows add functional-dependency signals in SKIN and LARGE_INTESTINE.
