Q-omics provides the consensus-scored AADACL2 profile across patient tissues and cancer cell-line models. AADACL2 expression is associated with patient survival in 16 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, AADACL2 is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, AADACL2 RNA expression shows 7,705 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KICH, COAD, and LSCC as cancer lineages where AADACL2 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 AADACL2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes AADACL2 survival associations across molecular data types. AADACL2 RNA expression shows survival associations in the most cancer types (16), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible AADACL2 RNA expression–survival associations across cancer types. High AADACL2 expression shows unfavorable associations in KICH, BLCA, SKCM, COAD and KIRC, but favorable associations in LUSC. The KICH 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 KICH as the clearest survival context for AADACL2 RNA expression.
This table summarizes AADACL2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10. The strongest signals are observed in COAD for RNA.
This table ranks reproducible tumor–normal expression differences for AADACL2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. AADACL2 shows lower tumor expression in COAD, HNSC, READ, STAD and LUAD and higher tumor expression in LUSC. The COAD box plot shows higher AADACL2 RNA expression in normal versus tumor tissue (log2 FC = −0.373, t-test p < 0.001).
This table shows molecular features associated with AADACL2 in patient tissues and cancer cell lines. In patient samples, AADACL2 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, AADACL2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and LARGE_INTESTINE.
