Q-omics provides the consensus-scored MAEA profile across patient tissues and cancer cell-line models. MAEA expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in UCEC. Among the 18 cancer types available for tumor–normal comparison, MAEA is differentially expressed in 8, with the highest sampling consensus in LIHC. Additionally, MAEA RNA expression shows 19,185 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight UCEC, LIHC, and UVM as cancer lineages where MAEA 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 MAEA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MAEA survival associations across molecular data types. MAEA RNA expression shows survival associations in the most cancer types (27), followed by mutation status (6) 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 MAEA RNA expression–survival associations across cancer types. High MAEA expression shows unfavorable associations in LGG, LUSC, LIHC and UVM, but favorable associations in UCEC and KIRP. The UCEC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .002). Together, the overview and detailed table identify UCEC as the clearest survival context for MAEA RNA expression.
This table summarizes MAEA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 3. The strongest signals are observed in LIHC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for MAEA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MAEA shows higher tumor expression in LIHC, LUAD, STAD, CHOL, LUSC and HNSC. The LIHC box plot shows higher MAEA RNA expression in tumor versus normal tissue (log2 FC = +1.169, t-test p < 0.001).
This table shows molecular features associated with MAEA in patient tissues and cancer cell lines. In patient samples, MAEA shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, MAEA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BREAST, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BLOOD_Lymphoma.
