Q-omics provides the consensus-scored NRSN2 profile across patient tissues and cancer cell-line models. NRSN2 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, NRSN2 is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, NRSN2 protein abundance shows 21,188 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight UVM, KICH, and PDAC as cancer lineages where NRSN2 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 NRSN2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NRSN2 survival associations across molecular data types. NRSN2 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (3) 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 NRSN2 RNA expression–survival associations across cancer types. High NRSN2 expression shows unfavorable associations in UVM, BLCA, KIRC, LUSC and LIHC, but favorable associations in PAAD. 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 NRSN2 RNA expression.
This table summarizes NRSN2 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 4. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for NRSN2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NRSN2 shows lower tumor expression in KICH and higher tumor expression in HNSC, KIRC, BRCA, CHOL and LIHC. The KICH box plot shows higher NRSN2 RNA expression in normal versus tumor tissue (log2 FC = −1.581, t-test p < 0.001).
This table shows molecular features associated with NRSN2 in patient tissues and cancer cell lines. In patient samples, NRSN2 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, NRSN2 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 UPPER_AERODIGESTIVE_TRACT and LUNG_NSCLC_LUAD.
