Q-omics provides the consensus-scored AGMAT profile across patient tissues and cancer cell-line models. AGMAT expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, AGMAT is differentially expressed in 17, with the highest sampling consensus in KIRP. Additionally, AGMAT RNA expression shows 18,548 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, KIRP, and UVM as cancer lineages where AGMAT 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 AGMAT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AGMAT survival associations across molecular data types. AGMAT RNA expression shows survival associations in the most cancer types (23), followed by mutation status (2) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AGMAT RNA expression–survival associations across cancer types. High AGMAT expression shows unfavorable associations in ACC, LUAD and MESO, but favorable associations in KIRC, STAD and KIRP. 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 AGMAT RNA expression.
This table summarizes AGMAT tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 9. The strongest signals are observed in LUAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for AGMAT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AGMAT shows lower tumor expression in KIRP and KICH and higher tumor expression in LUAD, BLCA, STAD and LUSC. The KIRP box plot shows higher AGMAT RNA expression in normal versus tumor tissue (log2 FC = −4.264, t-test p < 0.001).
This table shows molecular features associated with AGMAT in patient tissues and cancer cell lines. In patient samples, AGMAT shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, AGMAT RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BONE and LIVER.
