Q-omics provides the consensus-scored ADGB profile across patient tissues and cancer cell-line models. ADGB expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, ADGB is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, ADGB protein abundance shows 14,973 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight HNSC, and KIRC as cancer lineages where ADGB 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 ADGB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ADGB survival associations across molecular data types. ADGB RNA expression shows survival associations in the most cancer types (22), followed by mutation status (7) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ADGB RNA expression–survival associations across cancer types. High ADGB expression shows unfavorable associations in THCA and UCS, but favorable associations in HNSC, BRCA, COAD and PAAD. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .003). Together, the overview and detailed table identify HNSC as the clearest survival context for ADGB RNA expression.
This table summarizes ADGB 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 5. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ADGB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ADGB shows lower tumor expression in KIRC, LUSC, LUAD, READ, COAD and KICH. The KIRC box plot shows higher ADGB RNA expression in normal versus tumor tissue (log2 FC = −0.068, t-test p < 0.001).
This table shows molecular features associated with ADGB in patient tissues and cancer cell lines. In patient samples, ADGB shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, ADGB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in LIVER and BLOOD_Leukemia.
