Q-omics provides the consensus-scored PCCA profile across patient tissues and cancer cell-line models. PCCA expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PCCA is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, PCCA protein abundance shows 22,347 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, and GBM as cancer lineages where PCCA 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 PCCA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PCCA survival associations across molecular data types. PCCA RNA expression shows survival associations in the most cancer types (25), followed by mutation status (5) 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 PCCA RNA expression–survival associations across cancer types. High PCCA expression shows unfavorable associations in ACC and LUSC, but favorable associations in KIRC, KIRP, UCEC and LGG. 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 PCCA RNA expression.
This table summarizes PCCA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PCCA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PCCA shows lower tumor expression in KIRC, THCA, KIRP, HNSC, KICH and LUSC. The KIRC box plot shows higher PCCA RNA expression in normal versus tumor tissue (log2 FC = −1.428, t-test p < 0.001).
This table shows molecular features associated with PCCA in patient tissues and cancer cell lines. In patient samples, PCCA 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, PCCA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and UPPER_AERODIGESTIVE_TRACT.
