Q-omics provides the consensus-scored NIPSNAP2 profile across patient tissues and cancer cell-line models. NIPSNAP2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, NIPSNAP2 is differentially expressed in 7, with the highest sampling consensus in THCA. Additionally, NIPSNAP2 RNA expression shows 19,550 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight CESC, THCA, and ACC as cancer lineages where NIPSNAP2 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 NIPSNAP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NIPSNAP2 survival associations across molecular data types. NIPSNAP2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7) 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 NIPSNAP2 RNA expression–survival associations across cancer types. High NIPSNAP2 expression shows unfavorable associations in CESC, UVM, SCLC, HNSC and LIHC, but favorable associations in KIRP. The CESC 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 CESC as the clearest survival context for NIPSNAP2 RNA expression.
This table summarizes NIPSNAP2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 7, while mass-spec protein shows differences in 4. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for NIPSNAP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NIPSNAP2 shows lower tumor expression in THCA, KIRC and BLCA and higher tumor expression in LIHC, CHOL and KIRP. The THCA box plot shows higher NIPSNAP2 RNA expression in normal versus tumor tissue (log2 FC = −1.426, t-test p < 0.001).
This table shows molecular features associated with NIPSNAP2 in patient tissues and cancer cell lines. In patient samples, NIPSNAP2 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, NIPSNAP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Lymphoma.
