Q-omics provides the consensus-scored MCEE profile across patient tissues and cancer cell-line models. MCEE expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, MCEE is differentially expressed in 9, with the highest sampling consensus in KIRP. Additionally, MCEE protein abundance shows 25,202 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight HNSC, and KIRP as cancer lineages where MCEE 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 MCEE — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MCEE survival associations across molecular data types. MCEE RNA expression shows survival associations in the most cancer types (22), followed by mutation status (3) and mass-spec protein abundance (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MCEE RNA expression–survival associations across cancer types. High MCEE expression shows unfavorable associations in UVM and KICH, but favorable associations in HNSC, LIHC, SKCM and LUSC. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify HNSC as the clearest survival context for MCEE RNA expression.
This table summarizes MCEE tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 9. The strongest signals are observed in KIRP for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for MCEE. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MCEE shows lower tumor expression in KIRP, KIRC, THCA, KICH, HNSC and UCEC. The KIRP box plot shows higher MCEE RNA expression in normal versus tumor tissue (log2 FC = −1.018, t-test p < 0.001).
This table shows molecular features associated with MCEE in patient tissues and cancer cell lines. In patient samples, MCEE shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, MCEE RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and UPPER_AERODIGESTIVE_TRACT.
