Q-omics provides the consensus-scored PSPN profile across patient tissues and cancer cell-line models. PSPN expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PSPN is differentially expressed in 8, with the highest sampling consensus in LIHC. Additionally, PSPN RNA expression shows 18,242 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and LIHC as cancer lineages where PSPN 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 PSPN — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PSPN survival associations across molecular data types. PSPN RNA expression shows survival associations in the most cancer types (20), followed by mutation status (4) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PSPN RNA expression–survival associations across cancer types. High PSPN expression shows unfavorable associations in ACC, LIHC, UCS and DLBC, but favorable associations in HNSC and PAAD. The ACC 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 ACC as the clearest survival context for PSPN RNA expression.
This table summarizes PSPN tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 8, while mass-spec protein shows differences in 1. The strongest signals are observed in LIHC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for PSPN. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PSPN shows lower tumor expression in LUAD, THCA and KICH and higher tumor expression in LIHC, CHOL and STAD. The LIHC box plot shows higher PSPN RNA expression in tumor versus normal tissue (log2 FC = +0.466, t-test p < 0.001).
This table shows molecular features associated with PSPN in patient tissues and cancer cell lines. In patient samples, PSPN 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, PSPN RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BONE.
