Q-omics provides the consensus-scored ADA2 profile across patient tissues and cancer cell-line models. ADA2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, ADA2 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, ADA2 protein abundance shows 25,969 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, KIRC, and LSCC as cancer lineages where ADA2 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 ADA2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ADA2 survival associations across molecular data types. ADA2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (7) 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 ADA2 RNA expression–survival associations across cancer types. High ADA2 expression shows unfavorable associations in LGG, but favorable associations in HNSC, SKCM, LUAD, CESC and SARC. 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 ADA2 RNA expression.
This table summarizes ADA2 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 9. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for ADA2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ADA2 shows lower tumor expression in COAD, LUSC, LUAD, KICH and PAAD and higher tumor expression in KIRC. The KIRC box plot shows higher ADA2 RNA expression in tumor versus normal tissue (log2 FC = +0.888, t-test p < 0.001).
This table shows molecular features associated with ADA2 in patient tissues and cancer cell lines. In patient samples, ADA2 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, ADA2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
