Q-omics provides the consensus-scored RPSA profile across patient tissues and cancer cell-line models. RPSA 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, RPSA is differentially expressed in 13, with the highest sampling consensus in LIHC. Additionally, RPSA protein abundance shows 31,308 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, LIHC, and GBM as cancer lineages where RPSA 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 RPSA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RPSA survival associations across molecular data types. RPSA 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 RPSA RNA expression–survival associations across cancer types. High RPSA expression shows unfavorable associations in LIHC, ACC, KIRC and KICH, but favorable associations in UVM and BRCA. 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 RPSA RNA expression.
This table summarizes RPSA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 7. The strongest signals are observed in LIHC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for RPSA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RPSA shows lower tumor expression in HNSC and higher tumor expression in LIHC, KIRP, KIRC, COAD and CHOL. The LIHC box plot shows higher RPSA RNA expression in tumor versus normal tissue (log2 FC = +1.695, t-test p < 0.001).
This table shows molecular features associated with RPSA in patient tissues and cancer cell lines. In patient samples, RPSA 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, RPSA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and UPPER_AERODIGESTIVE_TRACT.
