proline rich mitotic checkpoint control factorGenealiases: RCCP1 · TPRC
Q-omics provides the consensus-scored PRCC profile across patient tissues and cancer cell-line models. PRCC expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PRCC is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, PRCC protein abundance shows 24,519 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where PRCC 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 PRCC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PRCC survival associations across molecular data types. PRCC RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) 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 PRCC RNA expression–survival associations across cancer types. High PRCC expression shows unfavorable associations in ACC, KIRP, LIHC, KICH, UCEC and UVM. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify ACC as the clearest survival context for PRCC RNA expression.
This table summarizes PRCC tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PRCC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PRCC shows higher tumor expression in HNSC, BLCA, LUAD, KIRC, LIHC and STAD. The HNSC box plot shows higher PRCC RNA expression in tumor versus normal tissue (log2 FC = +1.063, t-test p < 0.001).
This table shows molecular features associated with PRCC in patient tissues and cancer cell lines. In patient samples, PRCC 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, PRCC RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and SOFT_TISSUE.
