Q-omics provides the consensus-scored MCAT profile across patient tissues and cancer cell-line models. MCAT expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, MCAT is differentially expressed in 16, with the highest sampling consensus in COAD. Additionally, MCAT RNA expression shows 18,305 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight SCLC, COAD, and ACC as cancer lineages where MCAT 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 MCAT — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MCAT survival associations across molecular data types. MCAT RNA expression shows survival associations in the most cancer types (22), followed by mutation status (2) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MCAT RNA expression–survival associations across cancer types. High MCAT expression shows unfavorable associations in ACC, LUAD, UVM and LIHC, but favorable associations in SCLC and READ. The SCLC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify SCLC as the clearest survival context for MCAT RNA expression.
This table summarizes MCAT tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 4. The strongest signals are observed in KICH for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for MCAT. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MCAT shows lower tumor expression in KICH and higher tumor expression in COAD, STAD, LUSC, LIHC and BRCA. The COAD box plot shows higher MCAT RNA expression in tumor versus normal tissue (log2 FC = +0.803, t-test p < 0.001).
This table shows molecular features associated with MCAT in patient tissues and cancer cell lines. In patient samples, MCAT 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, MCAT RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and LARGE_INTESTINE.
