Q-omics provides the consensus-scored PPP1R3F profile across patient tissues and cancer cell-line models. PPP1R3F expression is associated with patient survival in 18 of 34 cancer types, with the highest sampling consensus in COAD. Among the 18 cancer types available for tumor–normal comparison, PPP1R3F is differentially expressed in 9, with the highest sampling consensus in UCEC. Additionally, PPP1R3F protein abundance shows 20,781 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight COAD, UCEC, and GBM as cancer lineages where PPP1R3F 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 PPP1R3F — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PPP1R3F survival associations across molecular data types. PPP1R3F RNA expression shows survival associations in the most cancer types (18), followed by mutation status (7) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PPP1R3F RNA expression–survival associations across cancer types. High PPP1R3F expression shows unfavorable associations in COAD and BRCA, but favorable associations in CHOL, PAAD, SKCM and ACC. The COAD Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify COAD as the clearest survival context for PPP1R3F RNA expression.
This table summarizes PPP1R3F tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 4. The strongest signals are observed in LIHC for RNA and PDAC for protein.
This table ranks reproducible tumor–normal expression differences for PPP1R3F. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PPP1R3F shows lower tumor expression in UCEC, KICH, BLCA and HNSC and higher tumor expression in LIHC and CHOL. The UCEC box plot shows higher PPP1R3F RNA expression in normal versus tumor tissue (log2 FC = −1.390, t-test p < 0.001).
This table shows molecular features associated with PPP1R3F in patient tissues and cancer cell lines. In patient samples, PPP1R3F 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, PPP1R3F RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Leukemia.
