Q-omics provides the consensus-scored METTL2A profile across patient tissues and cancer cell-line models. METTL2A expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, METTL2A is differentially expressed in 17, with the highest sampling consensus in KIRP. Additionally, METTL2A RNA expression shows 20,179 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight UVM, KIRP, and ACC as cancer lineages where METTL2A 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 METTL2A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes METTL2A survival associations across molecular data types. METTL2A RNA expression shows survival associations in the most cancer types (29), followed by mutation status (5) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible METTL2A RNA expression–survival associations across cancer types. High METTL2A expression shows unfavorable associations in UVM, LIHC, ACC, KICH, CESC and BLCA. 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 METTL2A RNA expression.
This table summarizes METTL2A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 3. The strongest signals are observed in KIRP for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for METTL2A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. METTL2A shows lower tumor expression in THCA and higher tumor expression in KIRP, BLCA, LIHC, LUAD and STAD. The KIRP box plot shows higher METTL2A RNA expression in tumor versus normal tissue (log2 FC = +1.109, t-test p < 0.001).
This table shows molecular features associated with METTL2A in patient tissues and cancer cell lines. In patient samples, METTL2A 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, METTL2A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
