Q-omics provides the consensus-scored NAT9 profile across patient tissues and cancer cell-line models. NAT9 expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, NAT9 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, NAT9 RNA expression shows 18,045 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, HNSC, and ACC as cancer lineages where NAT9 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 NAT9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NAT9 survival associations across molecular data types. NAT9 RNA expression shows survival associations in the most cancer types (19), followed by mutation status (5) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NAT9 RNA expression–survival associations across cancer types. High NAT9 expression shows unfavorable associations in KIRC, LIHC, CESC and PRAD, but favorable associations in PAAD and READ. 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 NAT9 RNA expression.
This table summarizes NAT9 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 LSCC for protein.
This table ranks reproducible tumor–normal expression differences for NAT9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NAT9 shows higher tumor expression in HNSC, COAD, BLCA, LIHC, LUAD and KIRP. The HNSC box plot shows higher NAT9 RNA expression in tumor versus normal tissue (log2 FC = +1.203, t-test p < 0.001).
This table shows molecular features associated with NAT9 in patient tissues and cancer cell lines. In patient samples, NAT9 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, NAT9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and LARGE_INTESTINE.
