Q-omics provides the consensus-scored AAGAB profile across patient tissues and cancer cell-line models. AAGAB expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, AAGAB is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, AAGAB protein abundance shows 35,553 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, HNSC, and PDAC as cancer lineages where AAGAB 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 AAGAB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AAGAB survival associations across molecular data types. AAGAB RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) and mass-spec protein abundance (12). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AAGAB RNA expression–survival associations across cancer types. High AAGAB expression shows unfavorable associations in ACC, UVM, LGG, LUAD, HNSC and LAML. The ACC 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 ACC as the clearest survival context for AAGAB RNA expression.
This table summarizes AAGAB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 12. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for AAGAB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AAGAB shows lower tumor expression in KIRC and THCA and higher tumor expression in HNSC, BLCA, STAD and LIHC. The HNSC box plot shows higher AAGAB RNA expression in tumor versus normal tissue (log2 FC = +1.013, t-test p < 0.001).
This table shows molecular features associated with AAGAB in patient tissues and cancer cell lines. In patient samples, AAGAB 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, AAGAB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and BLOOD_Leukemia.
