Q-omics provides the consensus-scored NFYB profile across patient tissues and cancer cell-line models. NFYB expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in UCS. Among the 18 cancer types available for tumor–normal comparison, NFYB is differentially expressed in 10, with the highest sampling consensus in THCA. Additionally, NFYB protein abundance shows 28,485 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UCS, THCA, and LSCC as cancer lineages where NFYB 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 NFYB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NFYB survival associations across molecular data types. NFYB RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible NFYB RNA expression–survival associations across cancer types. High NFYB expression shows unfavorable associations in UVM, KIRP and STAD, but favorable associations in UCS, UCEC and SKCM. The UCS Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .002). Together, the overview and detailed table identify UCS as the clearest survival context for NFYB RNA expression.
This table summarizes NFYB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 9. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for NFYB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NFYB shows lower tumor expression in THCA, KICH and LUAD and higher tumor expression in LIHC, HNSC and COAD. The THCA box plot shows higher NFYB RNA expression in normal versus tumor tissue (log2 FC = −0.480, t-test p < 0.001).
This table shows molecular features associated with NFYB in patient tissues and cancer cell lines. In patient samples, NFYB 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, NFYB RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
