Q-omics provides the consensus-scored PDE6D profile across patient tissues and cancer cell-line models. PDE6D expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PDE6D is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, PDE6D RNA expression shows 18,828 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where PDE6D 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 PDE6D — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PDE6D survival associations across molecular data types. PDE6D RNA expression shows survival associations in the most cancer types (24), 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 PDE6D RNA expression–survival associations across cancer types. High PDE6D expression shows unfavorable associations in ACC, MESO, KIRP, READ and UCS, but favorable associations in BLCA. The ACC 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 ACC as the clearest survival context for PDE6D RNA expression.
This table summarizes PDE6D 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 6. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PDE6D. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PDE6D shows lower tumor expression in THCA and KICH and higher tumor expression in HNSC, LUAD, LIHC and STAD. The HNSC box plot shows higher PDE6D RNA expression in tumor versus normal tissue (log2 FC = +0.713, t-test p < 0.001).
This table shows molecular features associated with PDE6D in patient tissues and cancer cell lines. In patient samples, PDE6D 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, PDE6D RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BLOOD_Myeloma.
