Q-omics provides the consensus-scored PNMA1 profile across patient tissues and cancer cell-line models. PNMA1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PNMA1 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, PNMA1 RNA expression shows 20,316 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where PNMA1 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 PNMA1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PNMA1 survival associations across molecular data types. PNMA1 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (7) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PNMA1 RNA expression–survival associations across cancer types. High PNMA1 expression shows unfavorable associations in ACC, BLCA, LIHC and KICH, but favorable associations in BRCA and KIRC. 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 PNMA1 RNA expression.
This table summarizes PNMA1 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 4. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PNMA1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PNMA1 shows lower tumor expression in KIRC, KICH, READ and BLCA and higher tumor expression in HNSC and LIHC. The HNSC box plot shows higher PNMA1 RNA expression in tumor versus normal tissue (log2 FC = +1.278, t-test p < 0.001).
This table shows molecular features associated with PNMA1 in patient tissues and cancer cell lines. In patient samples, PNMA1 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, PNMA1 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 BLOOD_Lymphoma and LARGE_INTESTINE.
