Q-omics provides the consensus-scored AADAC profile across patient tissues and cancer cell-line models. AADAC expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, AADAC is differentially expressed in 11, with the highest sampling consensus in LUAD. Additionally, AADAC protein abundance shows 29,903 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRP, LUAD, and LSCC as cancer lineages where AADAC 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 AADAC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AADAC survival associations across molecular data types. AADAC RNA expression shows survival associations in the most cancer types (20), followed by mutation status (4) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AADAC RNA expression–survival associations across cancer types. High AADAC expression shows unfavorable associations in KIRP, STAD, KICH, LGG and HNSC, but favorable associations in OV. The KIRP 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 KIRP as the clearest survival context for AADAC RNA expression.
This table summarizes AADAC 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 9. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for AADAC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AADAC shows lower tumor expression in LUAD, THCA, BRCA, HNSC and LUSC and higher tumor expression in COAD. The LUAD box plot shows higher AADAC RNA expression in normal versus tumor tissue (log2 FC = −3.843, t-test p < 0.001).
This table shows molecular features associated with AADAC in patient tissues and cancer cell lines. In patient samples, AADAC 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, AADAC RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in OVARY and STOMACH.
