Q-omics provides the consensus-scored PROZ profile across patient tissues and cancer cell-line models. PROZ expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, PROZ is differentially expressed in 14, with the highest sampling consensus in KIRC. Additionally, PROZ protein abundance shows 15,972 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LIHC, KIRC, and GBM as cancer lineages where PROZ 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 PROZ — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PROZ survival associations across molecular data types. PROZ RNA expression shows survival associations in the most cancer types (23), followed by mutation status (3) 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 PROZ RNA expression–survival associations across cancer types. High PROZ expression shows unfavorable associations in UVM, CESC, STAD and KIRP, but favorable associations in LIHC and BLCA. The LIHC 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 LIHC as the clearest survival context for PROZ RNA expression.
This table summarizes PROZ tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for PROZ. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PROZ shows lower tumor expression in KIRC, KIRP and LIHC and higher tumor expression in COAD, BRCA and HNSC. The KIRC box plot shows higher PROZ RNA expression in normal versus tumor tissue (log2 FC = −2.426, t-test p < 0.001).
This table shows molecular features associated with PROZ in patient tissues and cancer cell lines. In patient samples, PROZ 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, PROZ RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Lymphoma.
