Q-omics provides the consensus-scored PGR profile across patient tissues and cancer cell-line models. PGR expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in UCEC. Among the 18 cancer types available for tumor–normal comparison, PGR is differentially expressed in 13, with the highest sampling consensus in BLCA. Additionally, PGR RNA expression shows 25,081 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight UCEC, BLCA, and LSCC as cancer lineages where PGR 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 PGR — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PGR survival associations across molecular data types. PGR RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PGR RNA expression–survival associations across cancer types. High PGR expression shows unfavorable associations in UVM, MESO and BLCA, but favorable associations in UCEC, BRCA and KIRC. The UCEC 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 UCEC as the clearest survival context for PGR RNA expression.
This table summarizes PGR tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 4. The strongest signals are observed in BLCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PGR. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PGR shows lower tumor expression in BLCA, LUAD, COAD, LUSC and KIRC and higher tumor expression in KICH. The BLCA box plot shows higher PGR RNA expression in normal versus tumor tissue (log2 FC = −1.662, t-test p < 0.001).
This table shows molecular features associated with PGR in patient tissues and cancer cell lines. In patient samples, PGR shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, PGR RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and BREAST.
