Q-omics provides the consensus-scored BPI profile across patient tissues and cancer cell-line models. BPI expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, BPI is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, BPI protein abundance shows 31,406 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight ACC, KIRC, and PDAC as cancer lineages where BPI 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 BPI — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes BPI survival associations across molecular data types. BPI RNA expression shows survival associations in the most cancer types (26), followed by mutation status (5) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible BPI RNA expression–survival associations across cancer types. High BPI expression shows unfavorable associations in ACC, STAD and KIRP, but favorable associations in LGG, BRCA and SKCM. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify ACC as the clearest survival context for BPI RNA expression.
This table summarizes BPI tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 10. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for BPI. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. BPI shows lower tumor expression in KIRC, KIRP, KICH, BLCA, THCA and UCEC. The KIRC box plot shows higher BPI RNA expression in normal versus tumor tissue (log2 FC = −0.638, t-test p < 0.001).
This table shows molecular features associated with BPI in patient tissues and cancer cell lines. In patient samples, BPI shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, BPI RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
