Q-omics provides the consensus-scored PIGL profile across patient tissues and cancer cell-line models. PIGL expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, PIGL is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, PIGL RNA expression shows 20,343 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight UVM, and HNSC as cancer lineages where PIGL 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 PIGL — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PIGL survival associations across molecular data types. PIGL RNA expression shows survival associations in the most cancer types (22), followed by mutation status (4) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PIGL RNA expression–survival associations across cancer types. High PIGL expression shows unfavorable associations in UVM, KIRC, KICH and LGG, but favorable associations in BRCA and SKCM. The UVM 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 UVM as the clearest survival context for PIGL RNA expression.
This table summarizes PIGL 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 2. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for PIGL. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PIGL shows lower tumor expression in KICH and higher tumor expression in HNSC, COAD, STAD, LIHC and BLCA. The HNSC box plot shows higher PIGL RNA expression in tumor versus normal tissue (log2 FC = +0.600, t-test p < 0.001).
This table shows molecular features associated with PIGL in patient tissues and cancer cell lines. In patient samples, PIGL shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, PIGL 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 SKIN and BLOOD_Leukemia.
