Q-omics provides the consensus-scored PACRG profile across patient tissues and cancer cell-line models. PACRG expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, PACRG is differentially expressed in 15, with the highest sampling consensus in KIRC. Additionally, PACRG RNA expression shows 15,906 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight UVM, KIRC, and THYM as cancer lineages where PACRG 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 PACRG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PACRG survival associations across molecular data types. PACRG RNA expression shows survival associations in the most cancer types (22), followed by mutation status (7) 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 PACRG RNA expression–survival associations across cancer types. High PACRG expression shows unfavorable associations in LUAD, but favorable associations in UVM, COAD, KIRC, KIRP and LIHC. The UVM Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .002). Together, the overview and detailed table identify UVM as the clearest survival context for PACRG RNA expression.
This table summarizes PACRG 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 KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for PACRG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PACRG shows lower tumor expression in KIRC, THCA, LUAD, KIRP, COAD and LUSC. The KIRC box plot shows higher PACRG RNA expression in normal versus tumor tissue (log2 FC = −2.213, t-test p < 0.001).
This table shows molecular features associated with PACRG in patient tissues and cancer cell lines. In patient samples, PACRG shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, PACRG RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and BLOOD_Leukemia.
