Q-omics provides the consensus-scored NIPBL profile across patient tissues and cancer cell-line models. NIPBL expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, NIPBL is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, NIPBL protein abundance shows 29,521 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KIRC, and HNSC as cancer lineages where NIPBL 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 NIPBL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NIPBL survival associations across molecular data types. NIPBL RNA expression shows survival associations in the most cancer types (26), followed by mutation status (11) 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 NIPBL RNA expression–survival associations across cancer types. High NIPBL expression shows unfavorable associations in KICH and MESO, but favorable associations in KIRC, UCS, HNSC and THYM. The KIRC 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 KIRC as the clearest survival context for NIPBL RNA expression.
This table summarizes NIPBL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 5. The strongest signals are observed in THCA for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for NIPBL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NIPBL shows lower tumor expression in THCA and higher tumor expression in HNSC, STAD, LIHC, CHOL and LUSC. The HNSC box plot shows higher NIPBL RNA expression in tumor versus normal tissue (log2 FC = +0.675, t-test p < 0.001).
This table shows molecular features associated with NIPBL in patient tissues and cancer cell lines. In patient samples, NIPBL shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, NIPBL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
