Q-omics provides the consensus-scored NOP53 profile across patient tissues and cancer cell-line models. NOP53 expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, NOP53 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, NOP53 RNA expression shows 17,958 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and KIRC as cancer lineages where NOP53 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 NOP53 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NOP53 survival associations across molecular data types. NOP53 RNA expression shows survival associations in the most cancer types (29), followed by mutation status (9) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NOP53 RNA expression–survival associations across cancer types. High NOP53 expression shows unfavorable associations in ACC and COAD, but favorable associations in UCEC, GBM, KIRC and LGG. 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 NOP53 RNA expression.
This table summarizes NOP53 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 8. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for NOP53. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NOP53 shows lower tumor expression in BLCA, LUAD, HNSC and UCEC and higher tumor expression in KIRC and LIHC. The KIRC box plot shows higher NOP53 RNA expression in tumor versus normal tissue (log2 FC = +0.899, t-test p < 0.001).
This table shows molecular features associated with NOP53 in patient tissues and cancer cell lines. In patient samples, NOP53 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, NOP53 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in SKIN and CNS.
