Q-omics provides the consensus-scored NASP profile across patient tissues and cancer cell-line models. NASP expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, NASP is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, NASP protein abundance shows 26,617 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where NASP 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 NASP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NASP survival associations across molecular data types. NASP RNA expression shows survival associations in the most cancer types (22), followed by mutation status (6) 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 NASP RNA expression–survival associations across cancer types. High NASP expression shows unfavorable associations in ACC, MESO, KIRC, LIHC, KIRP and UVM. 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 NASP RNA expression.
This table summarizes NASP 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 7. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for NASP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NASP shows higher tumor expression in HNSC, KIRC, LIHC, STAD, LUAD and LUSC. The HNSC box plot shows higher NASP RNA expression in tumor versus normal tissue (log2 FC = +1.004, t-test p < 0.001).
This table shows molecular features associated with NASP in patient tissues and cancer cell lines. In patient samples, NASP 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, NASP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
