Q-omics provides the consensus-scored FGL2 profile across patient tissues and cancer cell-line models. FGL2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, FGL2 is differentially expressed in 15, with the highest sampling consensus in BLCA. Additionally, FGL2 protein abundance shows 27,207 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, BLCA, and GBM as cancer lineages where FGL2 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 FGL2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes FGL2 survival associations across molecular data types. FGL2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (5) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible FGL2 RNA expression–survival associations across cancer types. High FGL2 expression shows unfavorable associations in UVM and LGG, but favorable associations in KIRC, SKCM, HNSC and CESC. The KIRC 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 KIRC as the clearest survival context for FGL2 RNA expression.
This table summarizes FGL2 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 11. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for FGL2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. FGL2 shows lower tumor expression in BLCA, KICH, COAD, KIRC, THCA and KIRP. The BLCA box plot shows higher FGL2 RNA expression in normal versus tumor tissue (log2 FC = −4.735, t-test p < 0.001).
This table shows molecular features associated with FGL2 in patient tissues and cancer cell lines. In patient samples, FGL2 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, FGL2 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 LIVER and UPPER_AERODIGESTIVE_TRACT.
