Q-omics provides the consensus-scored PGPEP1 profile across patient tissues and cancer cell-line models. PGPEP1 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PGPEP1 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, PGPEP1 protein abundance shows 23,202 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight KIRC, KICH, and HNSC as cancer lineages where PGPEP1 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 PGPEP1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PGPEP1 survival associations across molecular data types. PGPEP1 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (2) and mass-spec protein abundance (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PGPEP1 RNA expression–survival associations across cancer types. High PGPEP1 expression shows unfavorable associations in LGG, but favorable associations in KIRC, PAAD, KIRP, BRCA and CHOL. The KIRC 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 KIRC as the clearest survival context for PGPEP1 RNA expression.
This table summarizes PGPEP1 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 6. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PGPEP1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PGPEP1 shows lower tumor expression in KICH, BLCA, LUAD and KIRP and higher tumor expression in LIHC and THCA. The KICH box plot shows higher PGPEP1 RNA expression in normal versus tumor tissue (log2 FC = −1.591, t-test p < 0.001).
This table shows molecular features associated with PGPEP1 in patient tissues and cancer cell lines. In patient samples, PGPEP1 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, PGPEP1 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 BREAST and BLOOD_Leukemia.
