Q-omics provides the consensus-scored MYDGF profile across patient tissues and cancer cell-line models. MYDGF expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, MYDGF is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, MYDGF protein abundance shows 32,684 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, KIRC, and GBM as cancer lineages where MYDGF 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 MYDGF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MYDGF survival associations across molecular data types. MYDGF RNA expression shows survival associations in the most cancer types (23), followed by mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MYDGF RNA expression–survival associations across cancer types. High MYDGF expression shows unfavorable associations in UVM, ACC, KIRP, KICH and LGG, but favorable associations in 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 MYDGF RNA expression.
This table summarizes MYDGF tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 9. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for MYDGF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MYDGF shows higher tumor expression in KIRC, COAD, HNSC, BLCA, KIRP and LIHC. The KIRC box plot shows higher MYDGF RNA expression in tumor versus normal tissue (log2 FC = +1.573, t-test p < 0.001).
This table shows molecular features associated with MYDGF in patient tissues and cancer cell lines. In patient samples, MYDGF 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, MYDGF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and SKIN.
