Q-omics provides the consensus-scored PPIC profile across patient tissues and cancer cell-line models. PPIC expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, PPIC is differentially expressed in 12, with the highest sampling consensus in LUAD. Additionally, PPIC protein abundance shows 23,838 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, LUAD, and GBM as cancer lineages where PPIC 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 PPIC — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PPIC survival associations across molecular data types. PPIC RNA expression shows survival associations in the most cancer types (26), 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 PPIC RNA expression–survival associations across cancer types. High PPIC expression shows unfavorable associations in UVM, KICH, OV, LGG, LIHC and MESO. The UVM 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 UVM as the clearest survival context for PPIC RNA expression.
This table summarizes PPIC 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 LUAD for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for PPIC. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PPIC shows higher tumor expression in LUAD, LIHC, STAD, KIRP, BRCA and CHOL. The LUAD box plot shows higher PPIC RNA expression in tumor versus normal tissue (log2 FC = +0.639, t-test p < 0.001).
This table shows molecular features associated with PPIC in patient tissues and cancer cell lines. In patient samples, PPIC 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, PPIC 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 LUNG_NSCLC_LUSC and BONE.
