Q-omics provides the consensus-scored MBP profile across patient tissues and cancer cell-line models. MBP expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, MBP is differentially expressed in 12, with the highest sampling consensus in LUAD. Additionally, MBP protein abundance shows 20,186 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, LUAD, and GBM as cancer lineages where MBP 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 MBP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MBP survival associations across molecular data types. MBP RNA expression shows survival associations in the most cancer types (19), followed by mutation status (5) 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 MBP RNA expression–survival associations across cancer types. High MBP expression shows unfavorable associations in UVM, but favorable associations in KIRC, COAD, SARC, PAAD and SCLC. The KIRC 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 KIRC as the clearest survival context for MBP RNA expression.
This table summarizes MBP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for MBP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MBP shows lower tumor expression in LUAD, THCA, BRCA, LUSC and KIRC and higher tumor expression in STAD. The LUAD box plot shows higher MBP RNA expression in normal versus tumor tissue (log2 FC = −0.966, t-test p < 0.001).
This table shows molecular features associated with MBP in patient tissues and cancer cell lines. In patient samples, MBP shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, MBP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and SOFT_TISSUE.
