Q-omics provides the consensus-scored PTGIS profile across patient tissues and cancer cell-line models. PTGIS expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, PTGIS is differentially expressed in 16, with the highest sampling consensus in KICH. Additionally, PTGIS protein abundance shows 29,793 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRP, KICH, and LSCC as cancer lineages where PTGIS 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 PTGIS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PTGIS survival associations across molecular data types. PTGIS RNA expression shows survival associations in the most cancer types (21), followed by mutation status (6) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PTGIS RNA expression–survival associations across cancer types. High PTGIS expression shows unfavorable associations in KIRP, BLCA, LUSC and MESO, but favorable associations in DLBC and SKCM. 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 PTGIS RNA expression.
This table summarizes PTGIS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 9. The strongest signals are observed in KICH for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PTGIS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PTGIS shows lower tumor expression in KICH, UCEC, BLCA, KIRC, THCA and LUSC. The KICH box plot shows higher PTGIS RNA expression in normal versus tumor tissue (log2 FC = −3.249, t-test p < 0.001).
This table shows molecular features associated with PTGIS in patient tissues and cancer cell lines. In patient samples, PTGIS 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, PTGIS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in OVARY and LARGE_INTESTINE.
