Q-omics provides the consensus-scored RPRML profile across patient tissues and cancer cell-line models. RPRML expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, RPRML is differentially expressed in 6, with the highest sampling consensus in HNSC. Additionally, RPRML RNA expression shows 11,853 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight CESC, HNSC, and TGCT as cancer lineages where RPRML 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 RPRML — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RPRML survival associations across molecular data types. RPRML RNA expression shows survival associations in the most cancer types (20), followed by mutation status (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RPRML RNA expression–survival associations across cancer types. High RPRML expression shows unfavorable associations in COAD, KIRP, UCS and READ, but favorable associations in CESC and SCLC. The CESC 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 CESC as the clearest survival context for RPRML RNA expression.
This table summarizes RPRML tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 6. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for RPRML. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RPRML shows lower tumor expression in HNSC, STAD, THCA and LUSC and higher tumor expression in LIHC and PRAD. The HNSC box plot shows higher RPRML RNA expression in normal versus tumor tissue (log2 FC = −0.185, t-test p < 0.001).
This table shows molecular features associated with RPRML in patient tissues and cancer cell lines. In patient samples, RPRML 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, RPRML RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and SOFT_TISSUE.
