Q-omics provides the consensus-scored ONECUT2 profile across patient tissues and cancer cell-line models. ONECUT2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ONECUT2 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, ONECUT2 RNA expression shows 18,601 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, HNSC, and UVM as cancer lineages where ONECUT2 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 ONECUT2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ONECUT2 survival associations across molecular data types. ONECUT2 RNA expression shows survival associations in the most cancer types (26), 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 ONECUT2 RNA expression–survival associations across cancer types. High ONECUT2 expression shows unfavorable associations in KIRC, UCEC, ACC, KICH and BLCA, but favorable associations in HNSC. The KIRC 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 KIRC as the clearest survival context for ONECUT2 RNA expression.
This table summarizes ONECUT2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 1. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ONECUT2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ONECUT2 shows lower tumor expression in THCA and higher tumor expression in HNSC, LUAD, STAD, LIHC and KIRP. The HNSC box plot shows higher ONECUT2 RNA expression in tumor versus normal tissue (log2 FC = +0.381, t-test p < 0.001).
This table shows molecular features associated with ONECUT2 in patient tissues and cancer cell lines. In patient samples, ONECUT2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, ONECUT2 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 BLOOD_Myeloma and LUNG_SCLC.
