Q-omics provides the consensus-scored NACC1 profile across patient tissues and cancer cell-line models. NACC1 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, NACC1 is differentially expressed in 17, with the highest sampling consensus in HNSC. Additionally, NACC1 protein abundance shows 25,025 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where NACC1 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 NACC1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NACC1 survival associations across molecular data types. NACC1 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) 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 NACC1 RNA expression–survival associations across cancer types. High NACC1 expression shows unfavorable associations in ACC, UCS, KIRC, SKCM and UVM, but favorable associations in SCLC. 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 NACC1 RNA expression.
This table summarizes NACC1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for NACC1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NACC1 shows higher tumor expression in HNSC, KIRP, LIHC, STAD, LUSC and LUAD. The HNSC box plot shows higher NACC1 RNA expression in tumor versus normal tissue (log2 FC = +1.378, t-test p < 0.001).
This table shows molecular features associated with NACC1 in patient tissues and cancer cell lines. In patient samples, NACC1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, NACC1 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 CNS and LARGE_INTESTINE.
