Q-omics provides the consensus-scored PPP1R16A profile across patient tissues and cancer cell-line models. PPP1R16A expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, PPP1R16A is differentially expressed in 13, with the highest sampling consensus in COAD. Additionally, PPP1R16A RNA expression shows 17,464 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRP, COAD, and TGCT as cancer lineages where PPP1R16A 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 PPP1R16A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PPP1R16A survival associations across molecular data types. PPP1R16A RNA expression shows survival associations in the most cancer types (20), 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 PPP1R16A RNA expression–survival associations across cancer types. High PPP1R16A expression shows unfavorable associations in UVM, LGG, UCS and LIHC, but favorable associations in KIRP and MESO. The KIRP 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 KIRP as the clearest survival context for PPP1R16A RNA expression.
This table summarizes PPP1R16A tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 4. The strongest signals are observed in COAD for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PPP1R16A. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PPP1R16A shows higher tumor expression in COAD, LIHC, KIRC, BLCA, LUAD and STAD. The COAD box plot shows higher PPP1R16A RNA expression in tumor versus normal tissue (log2 FC = +0.972, t-test p < 0.001).
This table shows molecular features associated with PPP1R16A in patient tissues and cancer cell lines. In patient samples, PPP1R16A 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, PPP1R16A 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 SKIN and SOFT_TISSUE.
