Q-omics provides the consensus-scored NFYA profile across patient tissues and cancer cell-line models. NFYA expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, NFYA is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, NFYA protein abundance shows 20,966 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight LIHC, HNSC, and LSCC as cancer lineages where NFYA 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 NFYA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NFYA survival associations across molecular data types. NFYA RNA expression shows survival associations in the most cancer types (28), 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 NFYA RNA expression–survival associations across cancer types. High NFYA expression shows unfavorable associations in LIHC, ACC, MESO, UVM and CESC, but favorable associations in UCS. 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 NFYA RNA expression.
This table summarizes NFYA 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 5. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for NFYA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NFYA shows lower tumor expression in KICH and higher tumor expression in HNSC, COAD, LUAD, LIHC and STAD. The HNSC box plot shows higher NFYA RNA expression in tumor versus normal tissue (log2 FC = +1.212, t-test p < 0.001).
This table shows molecular features associated with NFYA in patient tissues and cancer cell lines. In patient samples, NFYA 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, NFYA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and BLOOD_Leukemia.
