Q-omics provides the consensus-scored PIGQ profile across patient tissues and cancer cell-line models. PIGQ expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in DLBC. Among the 18 cancer types available for tumor–normal comparison, PIGQ is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, PIGQ protein abundance shows 21,906 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight DLBC, COAD, and LSCC as cancer lineages where PIGQ 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 PIGQ — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PIGQ survival associations across molecular data types. PIGQ RNA expression shows survival associations in the most cancer types (21), followed by mutation status (5) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PIGQ RNA expression–survival associations across cancer types. High PIGQ expression shows unfavorable associations in LGG and LIHC, but favorable associations in DLBC, PCPG, SCLC and OV. The DLBC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .002). Together, the overview and detailed table identify DLBC as the clearest survival context for PIGQ RNA expression.
This table summarizes PIGQ tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for PIGQ. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PIGQ shows lower tumor expression in KIRC and higher tumor expression in COAD, LIHC, HNSC, BRCA and STAD. The COAD box plot shows higher PIGQ RNA expression in tumor versus normal tissue (log2 FC = +0.809, t-test p < 0.001).
This table shows molecular features associated with PIGQ in patient tissues and cancer cell lines. In patient samples, PIGQ shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, PIGQ RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in STOMACH and SOFT_TISSUE.
