Q-omics provides the consensus-scored ATXN3 profile across patient tissues and cancer cell-line models. ATXN3 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ATXN3 is differentially expressed in 9, with the highest sampling consensus in HNSC. Additionally, ATXN3 RNA expression shows 21,293 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where ATXN3 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 ATXN3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ATXN3 survival associations across molecular data types. ATXN3 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (3) 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 ATXN3 RNA expression–survival associations across cancer types. High ATXN3 expression shows unfavorable associations in ACC, UVM and HNSC, but favorable associations in BRCA, THYM and LAML. 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 ATXN3 RNA expression.
This table summarizes ATXN3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ATXN3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ATXN3 shows lower tumor expression in KICH, LUAD and BRCA and higher tumor expression in HNSC, LIHC and CHOL. The HNSC box plot shows higher ATXN3 RNA expression in tumor versus normal tissue (log2 FC = +0.480, t-test p < 0.001).
This table shows molecular features associated with ATXN3 in patient tissues and cancer cell lines. In patient samples, ATXN3 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, ATXN3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BLOOD_Leukemia.
