Q-omics provides the consensus-scored ADAT3 profile across patient tissues and cancer cell-line models. ADAT3 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ADAT3 is differentially expressed in 10, with the highest sampling consensus in STAD. Additionally, ADAT3 RNA expression shows 16,170 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and STAD as cancer lineages where ADAT3 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 ADAT3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ADAT3 survival associations across molecular data types. ADAT3 RNA expression shows survival associations in the most cancer types (23), 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 ADAT3 RNA expression–survival associations across cancer types. High ADAT3 expression shows unfavorable associations in ACC, UVM and LGG, but favorable associations in STAD, UCEC and SCLC. 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 ADAT3 RNA expression.
This table summarizes ADAT3 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 LUSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for ADAT3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ADAT3 shows higher tumor expression in STAD, LUSC, BLCA, LIHC, COAD and BRCA. The STAD box plot shows higher ADAT3 RNA expression in tumor versus normal tissue (log2 FC = +0.919, t-test p < 0.001).
This table shows molecular features associated with ADAT3 in patient tissues and cancer cell lines. In patient samples, ADAT3 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, ADAT3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and BLOOD_Lymphoma.
