negative elongation factor complex member BGenealiases: COBRA1 · NELF-B
Q-omics provides the consensus-scored NELFB profile across patient tissues and cancer cell-line models. NELFB expression is associated with patient survival in 30 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, NELFB is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, NELFB protein abundance shows 27,540 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where NELFB 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 NELFB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NELFB survival associations across molecular data types. NELFB RNA expression shows survival associations in the most cancer types (30), followed by mutation status (5) 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 NELFB RNA expression–survival associations across cancer types. High NELFB expression shows unfavorable associations in ACC, MESO and LIHC, but favorable associations in UCEC, KIRC and UVM. The ACC 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 ACC as the clearest survival context for NELFB RNA expression.
This table summarizes NELFB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for NELFB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NELFB shows higher tumor expression in HNSC, COAD, LIHC, STAD, KIRP and CHOL. The HNSC box plot shows higher NELFB RNA expression in tumor versus normal tissue (log2 FC = +0.926, t-test p < 0.001).
This table shows molecular features associated with NELFB in patient tissues and cancer cell lines. In patient samples, NELFB 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, NELFB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and BLOOD_Lymphoma.
