Q-omics provides the consensus-scored APIP profile across patient tissues and cancer cell-line models. APIP expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in READ. Among the 18 cancer types available for tumor–normal comparison, APIP is differentially expressed in 12, with the highest sampling consensus in THCA. Additionally, APIP RNA expression shows 19,182 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight READ, THCA, and ACC as cancer lineages where APIP 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 APIP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APIP survival associations across molecular data types. APIP RNA expression shows survival associations in the most cancer types (24), followed by mutation status (3) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APIP RNA expression–survival associations across cancer types. High APIP expression shows unfavorable associations in LIHC, ACC and ESCA, but favorable associations in READ, SKCM and THCA. The READ Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify READ as the clearest survival context for APIP RNA expression.
This table summarizes APIP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 5. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for APIP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APIP shows lower tumor expression in THCA, LUAD, KIRP, LUSC and KIRC and higher tumor expression in LIHC. The THCA box plot shows higher APIP RNA expression in normal versus tumor tissue (log2 FC = −0.609, t-test p < 0.001).
This table shows molecular features associated with APIP in patient tissues and cancer cell lines. In patient samples, APIP 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, APIP 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 UPPER_AERODIGESTIVE_TRACT and BLOOD_Leukemia.
