Q-omics provides the consensus-scored MEF2D profile across patient tissues and cancer cell-line models. MEF2D expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, MEF2D is differentially expressed in 12, with the highest sampling consensus in BLCA. Additionally, MEF2D RNA expression shows 20,224 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, BLCA, and ACC as cancer lineages where MEF2D 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 MEF2D — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MEF2D survival associations across molecular data types. MEF2D RNA expression shows survival associations in the most cancer types (22), followed by mutation status (9) 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 MEF2D RNA expression–survival associations across cancer types. High MEF2D expression shows unfavorable associations in UVM, ACC and LUSC, but favorable associations in KIRC, UCS and HNSC. 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 MEF2D RNA expression.
This table summarizes MEF2D tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in BLCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for MEF2D. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MEF2D shows lower tumor expression in BLCA, COAD and THCA and higher tumor expression in KIRC, LIHC and HNSC. The BLCA box plot shows higher MEF2D RNA expression in normal versus tumor tissue (log2 FC = −1.767, t-test p < 0.001).
This table shows molecular features associated with MEF2D in patient tissues and cancer cell lines. In patient samples, MEF2D shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, MEF2D RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BLOOD_Lymphoma.
