Q-omics provides the consensus-scored RBPMS profile across patient tissues and cancer cell-line models. RBPMS expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, RBPMS is differentially expressed in 14, with the highest sampling consensus in BLCA. Additionally, RBPMS protein abundance shows 27,368 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UVM, BLCA, and LSCC as cancer lineages where RBPMS 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 RBPMS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RBPMS survival associations across molecular data types. RBPMS RNA expression shows survival associations in the most cancer types (21), 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 RBPMS RNA expression–survival associations across cancer types. High RBPMS expression shows unfavorable associations in LGG, ACC and LUSC, but favorable associations in UVM, KIRP and BLCA. The UVM 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 UVM as the clearest survival context for RBPMS RNA expression.
This table summarizes RBPMS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 5. The strongest signals are observed in BLCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for RBPMS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RBPMS shows lower tumor expression in BLCA, KICH, LUAD, BRCA and LUSC and higher tumor expression in KIRP. The BLCA box plot shows higher RBPMS RNA expression in normal versus tumor tissue (log2 FC = −2.932, t-test p < 0.001).
This table shows molecular features associated with RBPMS in patient tissues and cancer cell lines. In patient samples, RBPMS shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, RBPMS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and CNS.
