Q-omics provides the consensus-scored PIGW profile across patient tissues and cancer cell-line models. PIGW expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PIGW is differentially expressed in 17, with the highest sampling consensus in BLCA. Additionally, PIGW RNA expression shows 19,366 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and BLCA as cancer lineages where PIGW 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 PIGW — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PIGW survival associations across molecular data types. PIGW RNA expression shows survival associations in the most cancer types (26), followed by mutation status (2) 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 PIGW RNA expression–survival associations across cancer types. High PIGW expression shows unfavorable associations in ACC, UVM, MESO, KIRP and SKCM, but favorable associations in READ. The ACC 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 ACC as the clearest survival context for PIGW RNA expression.
This table summarizes PIGW tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17. The strongest signals are observed in BLCA for RNA.
This table ranks reproducible tumor–normal expression differences for PIGW. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PIGW shows lower tumor expression in THCA and higher tumor expression in BLCA, STAD, LUAD, HNSC and LIHC. The BLCA box plot shows higher PIGW RNA expression in tumor versus normal tissue (log2 FC = +1.457, t-test p < 0.001).
This table shows molecular features associated with PIGW in patient tissues and cancer cell lines. In patient samples, PIGW shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, PIGW RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and LARGE_INTESTINE.
