Q-omics provides the consensus-scored NEB profile across patient tissues and cancer cell-line models. NEB expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, NEB is differentially expressed in 13, with the highest sampling consensus in THCA. Additionally, NEB protein abundance shows 20,714 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KIRP, THCA, and HNSC as cancer lineages where NEB 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 NEB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NEB survival associations across molecular data types. NEB RNA expression shows survival associations in the most cancer types (24), followed by mutation status (14) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NEB RNA expression–survival associations across cancer types. High NEB expression shows unfavorable associations in KIRP, KICH, LIHC and LGG, but favorable associations in LUSC and MESO. The KIRP 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 KIRP as the clearest survival context for NEB RNA expression.
This table summarizes NEB 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 4. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for NEB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NEB shows lower tumor expression in THCA and KICH and higher tumor expression in LIHC, COAD, LUSC and KIRC. The THCA box plot shows higher NEB RNA expression in normal versus tumor tissue (log2 FC = −1.292, t-test p < 0.001).
This table shows molecular features associated with NEB in patient tissues and cancer cell lines. In patient samples, NEB 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, NEB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and UPPER_AERODIGESTIVE_TRACT.
