Q-omics provides the consensus-scored NPL profile across patient tissues and cancer cell-line models. NPL expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, NPL is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, NPL protein abundance shows 18,604 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight CESC, HNSC, and GBM as cancer lineages where NPL 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 NPL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NPL survival associations across molecular data types. NPL RNA expression shows survival associations in the most cancer types (23), followed by mutation status (3) 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 NPL RNA expression–survival associations across cancer types. High NPL expression shows unfavorable associations in ACC, LGG and LIHC, but favorable associations in CESC, LUSC and HNSC. The CESC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify CESC as the clearest survival context for NPL RNA expression.
This table summarizes NPL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for NPL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NPL shows lower tumor expression in KICH and KIRP and higher tumor expression in HNSC, KIRC, STAD and BRCA. The HNSC box plot shows higher NPL RNA expression in tumor versus normal tissue (log2 FC = +1.387, t-test p < 0.001).
This table shows molecular features associated with NPL in patient tissues and cancer cell lines. In patient samples, NPL 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, NPL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and BONE.
