Q-omics provides the consensus-scored PGPEP1L profile across patient tissues and cancer cell-line models. PGPEP1L expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in COAD. Among the 18 cancer types available for tumor–normal comparison, PGPEP1L is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, PGPEP1L RNA expression shows 10,781 significant gene co-expression associations, with the highest sampling consensus in SARC. Together, these results highlight COAD, KIRC, and SARC as cancer lineages where PGPEP1L 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 PGPEP1L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PGPEP1L survival associations across molecular data types. PGPEP1L RNA expression shows survival associations in the most cancer types (22), followed by mutation status (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PGPEP1L RNA expression–survival associations across cancer types. High PGPEP1L expression shows unfavorable associations in COAD, GBM and THCA, but favorable associations in LAML, HNSC and LGG. The COAD 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 COAD as the clearest survival context for PGPEP1L RNA expression.
This table summarizes PGPEP1L tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12. The strongest signals are observed in KIRC for RNA.
This table ranks reproducible tumor–normal expression differences for PGPEP1L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PGPEP1L shows lower tumor expression in KIRC, THCA, BLCA, STAD, LUAD and KIRP. The KIRC box plot shows higher PGPEP1L RNA expression in normal versus tumor tissue (log2 FC = −0.613, t-test p < 0.001).
This table shows molecular features associated with PGPEP1L in patient tissues and cancer cell lines. In patient samples, PGPEP1L shows the broadest associations at the RNA and protein expression levels, with SARC recurring as the lineage with the largest associated feature set. In cancer cell lines, PGPEP1L RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in UPPER_AERODIGESTIVE_TRACT and LARGE_INTESTINE.
