Q-omics provides the consensus-scored NMS profile across patient tissues and cancer cell-line models. NMS expression is associated with patient survival in 8 of 34 cancer types, with the highest sampling consensus in UVM. Additionally, NMS RNA expression shows 3,587 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight UVM, and STAD as cancer lineages where NMS 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 NMS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NMS survival associations across molecular data types. NMS RNA expression shows survival associations in the most cancer types (8), followed by mutation status (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NMS RNA expression–survival associations across cancer types. High NMS expression shows unfavorable associations in UVM, LIHC, KIRC, ACC, COAD and PCPG. The UVM 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 UVM as the clearest survival context for NMS RNA expression.
This table shows molecular features associated with NMS in patient tissues and cancer cell lines. In patient samples, NMS shows the broadest associations at the RNA and protein expression levels, with STAD recurring as the lineage with the largest associated feature set. In cancer cell lines, NMS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and BREAST.
