Q-omics provides the consensus-scored METTL2B profile across patient tissues and cancer cell-line models. METTL2B expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, METTL2B is differentially expressed in 16, with the highest sampling consensus in KIRP. Additionally, METTL2B RNA expression shows 19,318 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KICH, KIRP, and ACC as cancer lineages where METTL2B 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 METTL2B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes METTL2B survival associations across molecular data types. METTL2B RNA expression shows survival associations in the most cancer types (27), followed by mutation status (3) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible METTL2B RNA expression–survival associations across cancer types. High METTL2B expression shows unfavorable associations in KICH, UVM, LIHC, CESC, MESO and ACC. The KICH 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 KICH as the clearest survival context for METTL2B RNA expression.
This table summarizes METTL2B 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 LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for METTL2B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. METTL2B shows higher tumor expression in KIRP, LIHC, KIRC, COAD, LUAD and HNSC. The KIRP box plot shows higher METTL2B RNA expression in tumor versus normal tissue (log2 FC = +1.014, t-test p < 0.001).
This table shows molecular features associated with METTL2B in patient tissues and cancer cell lines. In patient samples, METTL2B 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, METTL2B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BLOOD_Leukemia.
