Q-omics provides the consensus-scored NPVF profile across patient tissues and cancer cell-line models. NPVF expression is associated with patient survival in 11 of 34 cancer types, with the highest sampling consensus in ACC. Additionally, NPVF protein abundance shows 8,140 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, and LSCC as cancer lineages where NPVF 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 NPVF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NPVF survival associations across molecular data types. NPVF RNA expression shows survival associations in the most cancer types (11), followed by mutation status (2) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NPVF RNA expression–survival associations across cancer types. High NPVF expression shows unfavorable associations in ACC, SCLC, ESCA, OV, LUSC 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 NPVF RNA expression.
This table shows molecular features associated with NPVF in patient tissues and cancer cell lines. In patient samples, NPVF shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, NPVF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and SOFT_TISSUE.
