Q-omics provides the consensus-scored NELFCD profile across patient tissues and cancer cell-line models. NELFCD expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, NELFCD is differentially expressed in 16, with the highest sampling consensus in COAD. Additionally, NELFCD protein abundance shows 26,449 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight LIHC, COAD, and LSCC as cancer lineages where NELFCD 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 NELFCD — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NELFCD survival associations across molecular data types. NELFCD RNA expression shows survival associations in the most cancer types (27), followed by mutation status (3) 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 NELFCD RNA expression–survival associations across cancer types. High NELFCD expression shows unfavorable associations in LIHC, UVM, ACC, KICH, LGG and SKCM. The LIHC 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 LIHC as the clearest survival context for NELFCD RNA expression.
This table summarizes NELFCD tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 9. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for NELFCD. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NELFCD shows lower tumor expression in THCA and higher tumor expression in COAD, HNSC, BLCA, STAD and LUSC. The COAD box plot shows higher NELFCD RNA expression in tumor versus normal tissue (log2 FC = +1.862, t-test p < 0.001).
This table shows molecular features associated with NELFCD in patient tissues and cancer cell lines. In patient samples, NELFCD shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, NELFCD 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 BONE.
