Q-omics provides the consensus-scored IPP profile across patient tissues and cancer cell-line models. IPP expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, IPP is differentially expressed in 8, with the highest sampling consensus in KICH. Additionally, IPP RNA expression shows 20,644 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, KICH, and ACC as cancer lineages where IPP 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 IPP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes IPP survival associations across molecular data types. IPP RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible IPP RNA expression–survival associations across cancer types. High IPP expression shows unfavorable associations in LGG and LIHC, but favorable associations in KIRC, COAD, CHOL and ACC. 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 IPP RNA expression.
This table summarizes IPP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 2. The strongest signals are observed in KICH for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for IPP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. IPP shows lower tumor expression in KICH and KIRP and higher tumor expression in LIHC, CHOL, LUAD and LUSC. The KICH box plot shows higher IPP RNA expression in normal versus tumor tissue (log2 FC = −1.377, t-test p < 0.001).
This table shows molecular features associated with IPP in patient tissues and cancer cell lines. In patient samples, IPP shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, IPP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and UPPER_AERODIGESTIVE_TRACT.
