Q-omics provides the consensus-scored MYOF profile across patient tissues and cancer cell-line models. MYOF expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, MYOF is differentially expressed in 11, with the highest sampling consensus in KIRP. Additionally, MYOF protein abundance shows 24,351 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, KIRP, and GBM as cancer lineages where MYOF 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 MYOF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MYOF survival associations across molecular data types. MYOF RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) 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 MYOF RNA expression–survival associations across cancer types. High MYOF expression shows unfavorable associations in UVM, LGG, PAAD and LAML, but favorable associations in BRCA and UCS. 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 MYOF RNA expression.
This table summarizes MYOF tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for MYOF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MYOF shows higher tumor expression in KIRP, KIRC, HNSC, LUAD, STAD and LIHC. The KIRP box plot shows higher MYOF RNA expression in tumor versus normal tissue (log2 FC = +1.450, t-test p < 0.001).
This table shows molecular features associated with MYOF in patient tissues and cancer cell lines. In patient samples, MYOF 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, MYOF 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 SOFT_TISSUE and BONE.
