Q-omics provides the consensus-scored PAPLN profile across patient tissues and cancer cell-line models. PAPLN expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in HNSC. Among the 18 cancer types available for tumor–normal comparison, PAPLN is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, PAPLN protein abundance shows 26,800 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight HNSC, and LSCC as cancer lineages where PAPLN 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 PAPLN — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PAPLN survival associations across molecular data types. PAPLN RNA expression shows survival associations in the most cancer types (23), followed by mutation status (7) 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 PAPLN RNA expression–survival associations across cancer types. High PAPLN expression shows unfavorable associations in UCEC and ACC, but favorable associations in HNSC, KIRP, LUAD and ESCA. The HNSC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify HNSC as the clearest survival context for PAPLN RNA expression.
This table summarizes PAPLN 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 6. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PAPLN. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PAPLN shows lower tumor expression in KICH, UCEC and BRCA and higher tumor expression in HNSC, THCA and LUAD. The HNSC box plot shows higher PAPLN RNA expression in tumor versus normal tissue (log2 FC = +1.850, t-test p < 0.001).
This table shows molecular features associated with PAPLN in patient tissues and cancer cell lines. In patient samples, PAPLN shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, PAPLN RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
