Q-omics provides the consensus-scored MFF profile across patient tissues and cancer cell-line models. MFF expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, MFF is differentially expressed in 15, with the highest sampling consensus in KICH. Additionally, MFF protein abundance shows 21,293 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, KICH, and GBM as cancer lineages where MFF 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 MFF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MFF survival associations across molecular data types. MFF RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MFF RNA expression–survival associations across cancer types. High MFF expression shows unfavorable associations in UVM, ACC, LIHC, LUAD, KIRP and UCEC. The UVM 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 UVM as the clearest survival context for MFF RNA expression.
This table summarizes MFF tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 6. The strongest signals are observed in KICH for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for MFF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MFF shows lower tumor expression in KICH and higher tumor expression in LIHC, LUAD, HNSC, CHOL and LUSC. The KICH box plot shows higher MFF RNA expression in normal versus tumor tissue (log2 FC = −1.339, t-test p < 0.001).
This table shows molecular features associated with MFF in patient tissues and cancer cell lines. In patient samples, MFF 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, MFF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and LUNG_NSCLC_LUAD.
