Q-omics provides the consensus-scored PIGR profile across patient tissues and cancer cell-line models. PIGR expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, PIGR is differentially expressed in 12, with the highest sampling consensus in KIRC. Additionally, PIGR protein abundance shows 18,490 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight KIRP, KIRC, and LUAD as cancer lineages where PIGR 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 PIGR — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PIGR survival associations across molecular data types. PIGR RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PIGR RNA expression–survival associations across cancer types. High PIGR expression shows unfavorable associations in ESCA, but favorable associations in KIRP, BRCA, MESO, READ and COAD. The KIRP 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 KIRP as the clearest survival context for PIGR RNA expression.
This table summarizes PIGR tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PIGR. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PIGR shows lower tumor expression in KIRC, LUAD, KICH, LUSC, COAD and BRCA. The KIRC box plot shows higher PIGR RNA expression in normal versus tumor tissue (log2 FC = −3.809, t-test p < 0.001).
This table shows molecular features associated with PIGR in patient tissues and cancer cell lines. In patient samples, PIGR shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, PIGR 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 SOFT_TISSUE and LARGE_INTESTINE.
