Q-omics provides the consensus-scored PRCP profile across patient tissues and cancer cell-line models. PRCP expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PRCP is differentially expressed in 10, with the highest sampling consensus in THCA. Additionally, PRCP protein abundance shows 21,397 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, THCA, and GBM as cancer lineages where PRCP 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 PRCP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PRCP survival associations across molecular data types. PRCP RNA expression shows survival associations in the most cancer types (20), followed by mutation status (4) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PRCP RNA expression–survival associations across cancer types. High PRCP expression shows unfavorable associations in LGG, SCLC and KICH, but favorable associations in KIRC, UVM and THCA. 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 PRCP RNA expression.
This table summarizes PRCP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 7. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PRCP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PRCP shows lower tumor expression in THCA, LUAD, UCEC, BLCA and KICH and higher tumor expression in HNSC. The THCA box plot shows higher PRCP RNA expression in normal versus tumor tissue (log2 FC = −1.022, t-test p < 0.001).
This table shows molecular features associated with PRCP in patient tissues and cancer cell lines. In patient samples, PRCP 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, PRCP 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_Lymphoma and LARGE_INTESTINE.
