Q-omics provides the consensus-scored MGMT profile across patient tissues and cancer cell-line models. MGMT expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in LUAD. Among the 18 cancer types available for tumor–normal comparison, MGMT is differentially expressed in 6, with the highest sampling consensus in KICH. Additionally, MGMT RNA expression shows 13,854 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight LUAD, KICH, and THYM as cancer lineages where MGMT 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 MGMT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MGMT survival associations across molecular data types. MGMT RNA expression shows survival associations in the most cancer types (26), followed by mutation status (3) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MGMT RNA expression–survival associations across cancer types. High MGMT expression shows unfavorable associations in GBM, ACC and LGG, but favorable associations in LUAD, COAD and LIHC. The LUAD Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify LUAD as the clearest survival context for MGMT RNA expression.
This table summarizes MGMT tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 6, while mass-spec protein shows differences in 3. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for MGMT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MGMT shows lower tumor expression in KICH, HNSC, CHOL, LIHC and ESCA and higher tumor expression in KIRC. The KICH box plot shows higher MGMT RNA expression in normal versus tumor tissue (log2 FC = −1.767, t-test p < 0.001).
This table shows molecular features associated with MGMT in patient tissues and cancer cell lines. In patient samples, MGMT shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, MGMT RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
