Q-omics provides the consensus-scored GML profile across patient tissues and cancer cell-line models. GML expression is associated with patient survival in 13 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, GML is differentially expressed in 3, with the highest sampling consensus in KIRC. Additionally, GML protein abundance shows 11,560 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight UVM, KIRC, and PDAC as cancer lineages where GML 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 GML — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GML survival associations across molecular data types. GML RNA expression shows survival associations in the most cancer types (13), followed by mutation status (3) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GML RNA expression–survival associations across cancer types. High GML expression shows unfavorable associations in UVM, KICH, LIHC, THYM and DLBC, but favorable associations in STAD. 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 GML RNA expression.
This table summarizes GML tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 3, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for GML. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GML shows higher tumor expression in KIRC, HNSC and BRCA. The KIRC box plot shows higher GML RNA expression in tumor versus normal tissue (log2 FC = +0.010, t-test p = .016).
This table shows molecular features associated with GML in patient tissues and cancer cell lines. In patient samples, GML shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, GML RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
