Q-omics provides the consensus-scored PGP profile across patient tissues and cancer cell-line models. PGP expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PGP is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, PGP protein abundance shows 20,750 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, HNSC, and GBM as cancer lineages where PGP 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 PGP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PGP survival associations across molecular data types. PGP RNA expression shows survival associations in the most cancer types (24), followed by mutation status (1) 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 PGP RNA expression–survival associations across cancer types. High PGP expression shows unfavorable associations in KIRC, UVM, ACC, LIHC and LUAD, but favorable associations in OV. The KIRC 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 KIRC as the clearest survival context for PGP RNA expression.
This table summarizes PGP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 4. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for PGP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PGP shows higher tumor expression in HNSC, LUAD, COAD, BLCA, LIHC and STAD. The HNSC box plot shows higher PGP RNA expression in tumor versus normal tissue (log2 FC = +0.859, t-test p < 0.001).
This table shows molecular features associated with PGP in patient tissues and cancer cell lines. In patient samples, PGP 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, PGP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SKIN and LARGE_INTESTINE.
