Q-omics provides the consensus-scored NFYC profile across patient tissues and cancer cell-line models. NFYC expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, NFYC is differentially expressed in 10, with the highest sampling consensus in THCA. Additionally, NFYC protein abundance shows 22,307 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, THCA, and GBM as cancer lineages where NFYC 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 NFYC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes NFYC survival associations across molecular data types. NFYC RNA expression shows survival associations in the most cancer types (19), followed by mutation status (4) 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 NFYC RNA expression–survival associations across cancer types. High NFYC expression shows unfavorable associations in ACC, KICH, LIHC and LGG, but favorable associations in UCS and KIRC. 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 NFYC RNA expression.
This table summarizes NFYC 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 5. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for NFYC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. NFYC shows lower tumor expression in THCA and KICH and higher tumor expression in LIHC, HNSC, STAD and BLCA. The THCA box plot shows higher NFYC RNA expression in normal versus tumor tissue (log2 FC = −0.805, t-test p < 0.001).
This table shows molecular features associated with NFYC in patient tissues and cancer cell lines. In patient samples, NFYC 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, NFYC 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 SKIN and BLOOD_Lymphoma.
