Q-omics provides the consensus-scored PSCA profile across patient tissues and cancer cell-line models. PSCA expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in BRCA. Among the 18 cancer types available for tumor–normal comparison, PSCA is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, PSCA RNA expression shows 11,332 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight BRCA, KIRC, and TGCT as cancer lineages where PSCA 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 PSCA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PSCA survival associations across molecular data types. PSCA RNA expression shows survival associations in the most cancer types (28), followed by mutation status (1) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PSCA RNA expression–survival associations across cancer types. High PSCA expression shows unfavorable associations in BRCA, KIRC, LUAD and PAAD, but favorable associations in CESC and BLCA. The BRCA 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 BRCA as the clearest survival context for PSCA RNA expression.
This table summarizes PSCA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 2. The strongest signals are observed in KIRC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for PSCA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PSCA shows lower tumor expression in KIRC, HNSC and KIRP and higher tumor expression in LIHC, BRCA and PAAD. The KIRC box plot shows higher PSCA RNA expression in normal versus tumor tissue (log2 FC = −2.291, t-test p < 0.001).
This table shows molecular features associated with PSCA in patient tissues and cancer cell lines. In patient samples, PSCA shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, PSCA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in BREAST and PANCREAS.
