Q-omics provides the consensus-scored NANP profile across patient tissues and cancer cell-line models. NANP expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, NANP is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, NANP RNA expression shows 19,284 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight UVM, and HNSC as cancer lineages where NANP 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 NANP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NANP survival associations across molecular data types. NANP RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NANP RNA expression–survival associations across cancer types. High NANP expression shows unfavorable associations in UVM, LIHC, KICH, LGG, BLCA and MESO. 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 NANP RNA expression.
This table summarizes NANP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 4. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for NANP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NANP shows lower tumor expression in THCA and higher tumor expression in HNSC, COAD, BLCA, STAD and LIHC. The HNSC box plot shows higher NANP RNA expression in tumor versus normal tissue (log2 FC = +1.018, t-test p < 0.001).
This table shows molecular features associated with NANP in patient tissues and cancer cell lines. In patient samples, NANP shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, NANP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BREAST.
