Q-omics provides the consensus-scored PPP4R3B profile across patient tissues and cancer cell-line models. PPP4R3B expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PPP4R3B is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, PPP4R3B RNA expression shows 21,022 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, HNSC, and ACC as cancer lineages where PPP4R3B 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 PPP4R3B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PPP4R3B survival associations across molecular data types. PPP4R3B RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4) 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 PPP4R3B RNA expression–survival associations across cancer types. High PPP4R3B expression shows unfavorable associations in ACC, CESC, KIRP and UVM, but favorable associations in KIRC and LUSC. The KIRC 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 KIRC as the clearest survival context for PPP4R3B RNA expression.
This table summarizes PPP4R3B 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 HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for PPP4R3B. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PPP4R3B shows lower tumor expression in THCA and KICH and higher tumor expression in HNSC, KIRC, STAD and BLCA. The HNSC box plot shows higher PPP4R3B RNA expression in tumor versus normal tissue (log2 FC = +0.724, t-test p < 0.001).
This table shows molecular features associated with PPP4R3B in patient tissues and cancer cell lines. In patient samples, PPP4R3B 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, PPP4R3B RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in KIDNEY and BLOOD_Leukemia.
