Q-omics provides the consensus-scored PPIG profile across patient tissues and cancer cell-line models. PPIG expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PPIG is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, PPIG protein abundance shows 32,428 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, HNSC, and GBM as cancer lineages where PPIG 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 PPIG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PPIG survival associations across molecular data types. PPIG RNA expression shows survival associations in the most cancer types (25), 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 PPIG RNA expression–survival associations across cancer types. High PPIG expression shows unfavorable associations in KIRP, ACC, LIHC and LGG, but favorable associations in KIRC and UCS. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for PPIG RNA expression.
This table summarizes PPIG 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 9. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for PPIG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PPIG shows lower tumor expression in THCA and KICH and higher tumor expression in HNSC, LIHC, CHOL and STAD. The HNSC box plot shows higher PPIG RNA expression in tumor versus normal tissue (log2 FC = +0.721, t-test p < 0.001).
This table shows molecular features associated with PPIG in patient tissues and cancer cell lines. In patient samples, PPIG 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, PPIG 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 BLOOD_Leukemia and LARGE_INTESTINE.
