Q-omics provides the consensus-scored DAXX profile across patient tissues and cancer cell-line models. DAXX expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, DAXX is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, DAXX protein abundance shows 21,145 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRP, HNSC, and GBM as cancer lineages where DAXX 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 DAXX — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DAXX survival associations across molecular data types. DAXX RNA expression shows survival associations in the most cancer types (26), followed by mutation status (7) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DAXX RNA expression–survival associations across cancer types. High DAXX expression shows unfavorable associations in KIRP, ACC, LIHC, COAD and LAML, but favorable associations in KIRC. The KIRP 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 KIRP as the clearest survival context for DAXX RNA expression.
This table summarizes DAXX tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for DAXX. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. DAXX shows higher tumor expression in HNSC, KIRC, KIRP, COAD, LIHC and STAD. The HNSC box plot shows higher DAXX RNA expression in tumor versus normal tissue (log2 FC = +0.864, t-test p < 0.001).
This table shows molecular features associated with DAXX in patient tissues and cancer cell lines. In patient samples, DAXX shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, DAXX RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BLOOD_Lymphoma.
