aspartylglucosaminidaseGenealiases: AGU · ASRG · GA
Q-omics provides the consensus-scored AGA profile across patient tissues and cancer cell-line models. AGA expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, AGA is differentially expressed in 10, with the highest sampling consensus in KICH. Additionally, AGA protein abundance shows 27,543 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, KICH, and LSCC as cancer lineages where AGA 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 AGA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AGA survival associations across molecular data types. AGA RNA expression shows survival associations in the most cancer types (28), followed by mutation status (4) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AGA RNA expression–survival associations across cancer types. High AGA expression shows unfavorable associations in HNSC, BLCA, LGG and LIHC, but favorable associations in KIRC and READ. The HNSC 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 HNSC as the clearest survival context for AGA RNA expression.
This table summarizes AGA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 10. The strongest signals are observed in KICH for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for AGA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AGA shows lower tumor expression in KICH and higher tumor expression in LIHC, BRCA, CHOL, LUAD and KIRC. The KICH box plot shows higher AGA RNA expression in normal versus tumor tissue (log2 FC = −1.388, t-test p < 0.001).
This table shows molecular features associated with AGA in patient tissues and cancer cell lines. In patient samples, AGA shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, AGA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BLOOD_Lymphoma.
