poly(A) polymerase alphaGenealiases: PAP · PAP-alpha
Q-omics provides the consensus-scored PAPOLA profile across patient tissues and cancer cell-line models. PAPOLA expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, PAPOLA is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, PAPOLA protein abundance shows 22,247 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRP, HNSC, and GBM as cancer lineages where PAPOLA 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 PAPOLA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PAPOLA survival associations across molecular data types. PAPOLA RNA expression shows survival associations in the most cancer types (25), followed by mutation status (7) 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 PAPOLA RNA expression–survival associations across cancer types. High PAPOLA expression shows unfavorable associations in KIRP, LIHC, ACC, STAD and HNSC, but favorable associations in KIRC. The KIRP 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 KIRP as the clearest survival context for PAPOLA RNA expression.
This table summarizes PAPOLA 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 10. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for PAPOLA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PAPOLA shows lower tumor expression in THCA and higher tumor expression in HNSC, BLCA, STAD, LIHC and BRCA. The HNSC box plot shows higher PAPOLA RNA expression in tumor versus normal tissue (log2 FC = +0.732, t-test p < 0.001).
This table shows molecular features associated with PAPOLA in patient tissues and cancer cell lines. In patient samples, PAPOLA 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, PAPOLA 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 LARGE_INTESTINE and BONE.
