Q-omics provides the consensus-scored PPIB profile across patient tissues and cancer cell-line models. PPIB expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, PPIB is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, PPIB protein abundance shows 28,748 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight UVM, HNSC, and GBM as cancer lineages where PPIB 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 PPIB — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PPIB survival associations across molecular data types. PPIB RNA expression shows survival associations in the most cancer types (28), followed by mutation status (3) 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 PPIB RNA expression–survival associations across cancer types. High PPIB expression shows unfavorable associations in UVM, HNSC, ACC, KICH and UCS, but favorable associations in UCEC. 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 PPIB RNA expression.
This table summarizes PPIB tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PPIB. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PPIB shows higher tumor expression in HNSC, KIRC, BLCA, LUAD, LIHC and COAD. The HNSC box plot shows higher PPIB RNA expression in tumor versus normal tissue (log2 FC = +1.441, t-test p < 0.001).
This table shows molecular features associated with PPIB in patient tissues and cancer cell lines. In patient samples, PPIB 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, PPIB 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 BREAST and BONE.
