Q-omics provides the consensus-scored PIP profile across patient tissues and cancer cell-line models. PIP expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PIP is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, PIP RNA expression shows 12,233 significant protein co-abundance associations, with the highest sampling consensus in BRCA. Together, these results highlight ACC, HNSC, and BRCA as cancer lineages where PIP 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 PIP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PIP survival associations across molecular data types. PIP RNA expression shows survival associations in the most cancer types (19), followed by mutation status (5) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PIP RNA expression–survival associations across cancer types. High PIP expression shows unfavorable associations in ACC, THCA, SCLC and UCS, but favorable associations in HNSC and KICH. The ACC 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 ACC as the clearest survival context for PIP RNA expression.
This table summarizes PIP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 2. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for PIP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PIP shows lower tumor expression in HNSC, LUSC and BRCA and higher tumor expression in KICH, BLCA and KIRP. The HNSC box plot shows higher PIP RNA expression in normal versus tumor tissue (log2 FC = −4.317, t-test p < 0.001).
This table shows molecular features associated with PIP in patient tissues and cancer cell lines. In patient samples, PIP shows the broadest associations at the RNA and protein expression levels, with BRCA recurring as the lineage with the largest associated feature set. In cancer cell lines, PIP 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 BREAST and SOFT_TISSUE.
