Q-omics provides the consensus-scored PPIF profile across patient tissues and cancer cell-line models. PPIF expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, PPIF is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, PPIF protein abundance shows 40,193 significant protein co-abundance associations, with the highest sampling consensus in LUAD. Together, these results highlight UVM, KICH, and LUAD as cancer lineages where PPIF 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 PPIF — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PPIF survival associations across molecular data types. PPIF RNA expression shows survival associations in the most cancer types (24), followed by mutation status (2) 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 PPIF RNA expression–survival associations across cancer types. High PPIF expression shows unfavorable associations in UVM, LUAD, HNSC, LAML and UCEC, but favorable associations in LGG. 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 PPIF RNA expression.
This table summarizes PPIF 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 12. The strongest signals are observed in KICH for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PPIF. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PPIF shows lower tumor expression in THCA and higher tumor expression in KICH, COAD, LUSC, LUAD and HNSC. The KICH box plot shows higher PPIF RNA expression in tumor versus normal tissue (log2 FC = +1.178, t-test p < 0.001).
This table shows molecular features associated with PPIF in patient tissues and cancer cell lines. In patient samples, PPIF shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, PPIF RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in OVARY and LARGE_INTESTINE.
