Q-omics provides the consensus-scored AIDA profile across patient tissues and cancer cell-line models. AIDA expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, AIDA is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, AIDA protein abundance shows 25,180 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, HNSC, and PDAC as cancer lineages where AIDA 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 AIDA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AIDA survival associations across molecular data types. AIDA RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AIDA RNA expression–survival associations across cancer types. High AIDA expression shows unfavorable associations in ACC, CESC, LGG, KIRP and SCLC, but favorable associations in KIRC. 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 AIDA RNA expression.
This table summarizes AIDA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for AIDA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AIDA shows lower tumor expression in KICH and higher tumor expression in HNSC, KIRC, LIHC, CHOL and KIRP. The HNSC box plot shows higher AIDA RNA expression in tumor versus normal tissue (log2 FC = +1.234, t-test p < 0.001).
This table shows molecular features associated with AIDA in patient tissues and cancer cell lines. In patient samples, AIDA 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, AIDA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
