Q-omics provides the consensus-scored MAGEB10 profile across patient tissues and cancer cell-line models. MAGEB10 expression is associated with patient survival in 15 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, MAGEB10 is differentially expressed in 3, with the highest sampling consensus in PRAD. Additionally, MAGEB10 RNA expression shows 8,112 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KICH, PRAD, and TGCT as cancer lineages where MAGEB10 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 MAGEB10 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MAGEB10 survival associations across molecular data types. MAGEB10 RNA expression shows survival associations in the most cancer types (15), followed by mutation status (8) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MAGEB10 RNA expression–survival associations across cancer types. High MAGEB10 expression shows unfavorable associations in KICH, LUSC, LIHC, READ, UCS and LUAD. 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 MAGEB10 RNA expression.
This table summarizes MAGEB10 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 3. The strongest signals are observed in LIHC for RNA.
This table ranks reproducible tumor–normal expression differences for MAGEB10. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MAGEB10 shows lower tumor expression in PRAD and higher tumor expression in LIHC and THCA. The PRAD box plot shows higher MAGEB10 RNA expression in normal versus tumor tissue (log2 FC = −0.034, t-test p = .010).
This table shows molecular features associated with MAGEB10 in patient tissues and cancer cell lines. In patient samples, MAGEB10 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, MAGEB10 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_Myeloma and BONE.
