Q-omics provides the consensus-scored NEBL profile across patient tissues and cancer cell-line models. NEBL expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, NEBL is differentially expressed in 13, with the highest sampling consensus in COAD. Additionally, NEBL protein abundance shows 33,776 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, COAD, and GBM as cancer lineages where NEBL 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 NEBL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NEBL survival associations across molecular data types. NEBL RNA expression shows survival associations in the most cancer types (25), followed by mutation status (10) and mass-spec protein abundance (12). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NEBL RNA expression–survival associations across cancer types. High NEBL expression shows unfavorable associations in BLCA, OV, LUAD and PAAD, but favorable associations in KIRC and KIRP. 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 NEBL RNA expression.
This table summarizes NEBL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 11. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for NEBL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NEBL shows lower tumor expression in LUAD, KIRC, THCA, KIRP and HNSC and higher tumor expression in COAD. The COAD box plot shows higher NEBL RNA expression in tumor versus normal tissue (log2 FC = +2.658, t-test p < 0.001).
This table shows molecular features associated with NEBL in patient tissues and cancer cell lines. In patient samples, NEBL shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, NEBL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in CNS and SOFT_TISSUE.
