Q-omics provides the consensus-scored NUDT16L1 profile across patient tissues and cancer cell-line models. NUDT16L1 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, NUDT16L1 is differentially expressed in 13, with the highest sampling consensus in LIHC. Additionally, NUDT16L1 RNA expression shows 18,315 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, LIHC, and ACC as cancer lineages where NUDT16L1 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 NUDT16L1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NUDT16L1 survival associations across molecular data types. NUDT16L1 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (1) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NUDT16L1 RNA expression–survival associations across cancer types. High NUDT16L1 expression shows unfavorable associations in UVM, LGG and ACC, but favorable associations in BRCA, LUAD and KIRC. The UVM 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 UVM as the clearest survival context for NUDT16L1 RNA expression.
This table summarizes NUDT16L1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 5. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for NUDT16L1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NUDT16L1 shows lower tumor expression in KICH and higher tumor expression in LIHC, BRCA, LUAD, CHOL and COAD. The LIHC box plot shows higher NUDT16L1 RNA expression in tumor versus normal tissue (log2 FC = +0.960, t-test p < 0.001).
This table shows molecular features associated with NUDT16L1 in patient tissues and cancer cell lines. In patient samples, NUDT16L1 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, NUDT16L1 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 BREAST and BLOOD_Lymphoma.
