Q-omics provides the consensus-scored PGC profile across patient tissues and cancer cell-line models. PGC expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, PGC is differentially expressed in 9, with the highest sampling consensus in LUAD. Additionally, PGC protein abundance shows 15,398 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight UVM, and LUAD as cancer lineages where PGC 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 PGC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PGC survival associations across molecular data types. PGC RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PGC RNA expression–survival associations across cancer types. High PGC expression shows unfavorable associations in UVM, ACC, HNSC and READ, but favorable associations in SKCM and LUAD. The UVM 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 UVM as the clearest survival context for PGC RNA expression.
This table summarizes PGC tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 5. The strongest signals are observed in LUAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PGC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PGC shows lower tumor expression in LUAD, LUSC and KIRC and higher tumor expression in COAD, LIHC and KIRP. The LUAD box plot shows higher PGC RNA expression in normal versus tumor tissue (log2 FC = −5.916, t-test p < 0.001).
This table shows molecular features associated with PGC in patient tissues and cancer cell lines. In patient samples, PGC shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, PGC RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in BREAST and LARGE_INTESTINE.
