Q-omics provides the consensus-scored PGLS profile across patient tissues and cancer cell-line models. PGLS 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, PGLS is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, PGLS protein abundance shows 37,314 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, KIRC, and GBM as cancer lineages where PGLS 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 PGLS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PGLS survival associations across molecular data types. PGLS RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) and mass-spec protein abundance (13). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PGLS RNA expression–survival associations across cancer types. High PGLS expression shows unfavorable associations in ACC, KIRC, LIHC, LGG, KIRP and UCS. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify ACC as the clearest survival context for PGLS RNA expression.
This table summarizes PGLS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 11. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for PGLS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PGLS shows higher tumor expression in KIRC, KIRP, COAD, LIHC, BRCA and HNSC. The KIRC box plot shows higher PGLS RNA expression in tumor versus normal tissue (log2 FC = +1.068, t-test p < 0.001).
This table shows molecular features associated with PGLS in patient tissues and cancer cell lines. In patient samples, PGLS 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, PGLS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and SOFT_TISSUE.
