Q-omics provides the consensus-scored PPCS profile across patient tissues and cancer cell-line models. PPCS expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, PPCS is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, PPCS protein abundance shows 26,188 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KICH, and GBM as cancer lineages where PPCS 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 PPCS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PPCS survival associations across molecular data types. PPCS RNA expression shows survival associations in the most cancer types (20), followed by mutation status (4) and mass-spec protein abundance (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PPCS RNA expression–survival associations across cancer types. High PPCS expression shows unfavorable associations in KICH, LIHC, LGG, PAAD and LAML, but favorable associations in MESO. The KICH 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 KICH as the clearest survival context for PPCS RNA expression.
This table summarizes PPCS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 7. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for PPCS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PPCS shows lower tumor expression in KICH and THCA and higher tumor expression in HNSC, BRCA, LIHC and CHOL. The KICH box plot shows higher PPCS RNA expression in normal versus tumor tissue (log2 FC = −1.384, t-test p < 0.001).
This table shows molecular features associated with PPCS in patient tissues and cancer cell lines. In patient samples, PPCS 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, PPCS 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.
