Q-omics provides the consensus-scored AGL profile across patient tissues and cancer cell-line models. AGL expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, AGL is differentially expressed in 11, with the highest sampling consensus in THCA. Additionally, AGL RNA expression shows 20,914 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, THCA, and UVM as cancer lineages where AGL 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 AGL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AGL survival associations across molecular data types. AGL RNA expression shows survival associations in the most cancer types (24), followed by mutation status (11) 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 AGL RNA expression–survival associations across cancer types. High AGL expression shows unfavorable associations in LGG, ACC and MESO, but favorable associations in KIRC, COAD and LUSC. 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 AGL RNA expression.
This table summarizes AGL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 4. The strongest signals are observed in THCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for AGL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AGL shows lower tumor expression in THCA, KIRC, COAD and LIHC and higher tumor expression in BRCA and LUAD. The THCA box plot shows higher AGL RNA expression in normal versus tumor tissue (log2 FC = −1.338, t-test p < 0.001).
This table shows molecular features associated with AGL in patient tissues and cancer cell lines. In patient samples, AGL 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, AGL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in OESOPHAGUS and LARGE_INTESTINE.
