Q-omics provides the consensus-scored MGLL profile across patient tissues and cancer cell-line models. MGLL expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, MGLL is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, MGLL protein abundance shows 27,205 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, HNSC, and LSCC as cancer lineages where MGLL 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 MGLL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MGLL survival associations across molecular data types. MGLL RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) 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 MGLL RNA expression–survival associations across cancer types. High MGLL expression shows unfavorable associations in UVM, ACC, PAAD, CESC and BLCA, but favorable associations in UCS. The UVM 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 UVM as the clearest survival context for MGLL RNA expression.
This table summarizes MGLL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 8. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for MGLL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MGLL shows lower tumor expression in HNSC, BLCA, COAD, LUSC and LUAD and higher tumor expression in KIRC. The HNSC box plot shows higher MGLL RNA expression in normal versus tumor tissue (log2 FC = −1.701, t-test p < 0.001).
This table shows molecular features associated with MGLL in patient tissues and cancer cell lines. In patient samples, MGLL shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, MGLL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BONE.
