Q-omics provides the consensus-scored GNMT profile across patient tissues and cancer cell-line models. GNMT expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, GNMT is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, GNMT RNA expression shows 18,389 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight HNSC, KIRC, and PDAC as cancer lineages where GNMT 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 GNMT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GNMT survival associations across molecular data types. GNMT RNA expression shows survival associations in the most cancer types (23), followed by mutation status (1) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GNMT RNA expression–survival associations across cancer types. High GNMT expression shows unfavorable associations in KIRC and ESCA, but favorable associations in HNSC, MESO, CESC and LUAD. The HNSC 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 HNSC as the clearest survival context for GNMT RNA expression.
This table summarizes GNMT 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 4. The strongest signals are observed in KIRC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for GNMT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GNMT shows lower tumor expression in KIRC, THCA, LUAD, LIHC and LUSC and higher tumor expression in COAD. The KIRC box plot shows higher GNMT RNA expression in normal versus tumor tissue (log2 FC = −0.765, t-test p < 0.001).
This table shows molecular features associated with GNMT in patient tissues and cancer cell lines. In patient samples, GNMT 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, GNMT RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
