Q-omics provides the consensus-scored PON1 profile across patient tissues and cancer cell-line models. PON1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PON1 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, PON1 protein abundance shows 19,596 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, and GBM as cancer lineages where PON1 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 PON1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PON1 survival associations across molecular data types. PON1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PON1 RNA expression–survival associations across cancer types. High PON1 expression shows unfavorable associations in KIRC and STAD, but favorable associations in LIHC, PAAD, MESO and LGG. The KIRC 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 KIRC as the clearest survival context for PON1 RNA expression.
This table summarizes PON1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for PON1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PON1 shows lower tumor expression in KIRC, KIRP, LIHC, KICH, CHOL and LUSC. The KIRC box plot shows higher PON1 RNA expression in normal versus tumor tissue (log2 FC = −0.353, t-test p < 0.001).
This table shows molecular features associated with PON1 in patient tissues and cancer cell lines. In patient samples, PON1 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, PON1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and LUNG_SCLC.
