Q-omics provides the consensus-scored AICDA profile across patient tissues and cancer cell-line models. AICDA 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, AICDA is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, AICDA protein abundance shows 16,471 significant protein co-abundance associations, with the highest sampling consensus in BRCA. Together, these results highlight HNSC, KIRC, and BRCA as cancer lineages where AICDA 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 AICDA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AICDA survival associations across molecular data types. AICDA RNA expression shows survival associations in the most cancer types (22), followed by mutation status (7) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AICDA RNA expression–survival associations across cancer types. High AICDA expression shows unfavorable associations in MESO and KIRP, but favorable associations in HNSC, ESCA, LUAD and STAD. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify HNSC as the clearest survival context for AICDA RNA expression.
This table summarizes AICDA 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 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for AICDA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AICDA shows lower tumor expression in COAD, LUSC and KICH and higher tumor expression in KIRC, KIRP and CHOL. The KIRC box plot shows higher AICDA RNA expression in tumor versus normal tissue (log2 FC = +0.650, t-test p < 0.001).
This table shows molecular features associated with AICDA in patient tissues and cancer cell lines. In patient samples, AICDA shows the broadest associations at the RNA and protein expression levels, with BRCA recurring as the lineage with the largest associated feature set. In cancer cell lines, AICDA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in CNS and BLOOD_Lymphoma.
