Q-omics provides the consensus-scored PGLYRP2 profile across patient tissues and cancer cell-line models. PGLYRP2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PGLYRP2 is differentially expressed in 11, with the highest sampling consensus in THCA. Additionally, PGLYRP2 protein abundance shows 25,033 significant protein co-abundance associations, with the highest sampling consensus in BRCA. Together, these results highlight KIRC, THCA, and BRCA as cancer lineages where PGLYRP2 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 PGLYRP2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PGLYRP2 survival associations across molecular data types. PGLYRP2 RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) 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 PGLYRP2 RNA expression–survival associations across cancer types. High PGLYRP2 expression shows unfavorable associations in KIRC and COAD, but favorable associations in HNSC, BRCA, BLCA and LUAD. 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 PGLYRP2 RNA expression.
This table summarizes PGLYRP2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 5. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for PGLYRP2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PGLYRP2 shows lower tumor expression in THCA, LIHC and CHOL and higher tumor expression in HNSC, BRCA and ESCA. The THCA box plot shows higher PGLYRP2 RNA expression in normal versus tumor tissue (log2 FC = −0.100, t-test p < 0.001).
This table shows molecular features associated with PGLYRP2 in patient tissues and cancer cell lines. In patient samples, PGLYRP2 shows the broadest associations at the RNA and protein expression levels, with BRCA recurring as the lineage with the largest associated feature set. In cancer cell lines, PGLYRP2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and BONE.
