Q-omics provides the consensus-scored PIGC profile across patient tissues and cancer cell-line models. PIGC expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, PIGC is differentially expressed in 15, with the highest sampling consensus in HNSC. Additionally, PIGC RNA expression shows 18,822 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRP, HNSC, and ACC as cancer lineages where PIGC 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 PIGC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PIGC survival associations across molecular data types. PIGC RNA expression shows survival associations in the most cancer types (25), followed by mutation status (4) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PIGC RNA expression–survival associations across cancer types. High PIGC expression shows unfavorable associations in KIRP, LIHC, ACC, LGG, UVM and STAD. The KIRP 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 KIRP as the clearest survival context for PIGC RNA expression.
This table summarizes PIGC tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for PIGC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PIGC shows lower tumor expression in KICH and higher tumor expression in HNSC, LUAD, LIHC, KIRC and KIRP. The HNSC box plot shows higher PIGC RNA expression in tumor versus normal tissue (log2 FC = +0.939, t-test p < 0.001).
This table shows molecular features associated with PIGC in patient tissues and cancer cell lines. In patient samples, PIGC 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, PIGC 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 SKIN and UPPER_AERODIGESTIVE_TRACT.
