Q-omics provides the consensus-scored NEFL profile across patient tissues and cancer cell-line models. NEFL expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, NEFL is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, NEFL protein abundance shows 24,857 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, HNSC, and GBM as cancer lineages where NEFL 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 NEFL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NEFL survival associations across molecular data types. NEFL RNA expression shows survival associations in the most cancer types (20), followed by mutation status (7) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NEFL RNA expression–survival associations across cancer types. High NEFL expression shows unfavorable associations in UVM, LUAD, THCA and KIRP, but favorable associations in ESCA and UCS. The UVM Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .003). Together, the overview and detailed table identify UVM as the clearest survival context for NEFL RNA expression.
This table summarizes NEFL tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for NEFL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NEFL shows lower tumor expression in COAD, KICH, READ and STAD and higher tumor expression in HNSC and KIRP. The HNSC box plot shows higher NEFL RNA expression in tumor versus normal tissue (log2 FC = +3.572, t-test p < 0.001).
This table shows molecular features associated with NEFL in patient tissues and cancer cell lines. In patient samples, NEFL 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, NEFL RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and UPPER_AERODIGESTIVE_TRACT.
