Q-omics provides the consensus-scored PHYH profile across patient tissues and cancer cell-line models. PHYH expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, PHYH is differentially expressed in 10, with the highest sampling consensus in COAD. Additionally, PHYH RNA expression shows 18,553 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, COAD, and UVM as cancer lineages where PHYH 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 PHYH — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PHYH survival associations across molecular data types. PHYH RNA expression shows survival associations in the most cancer types (21), followed by mutation status (3) 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 PHYH RNA expression–survival associations across cancer types. High PHYH expression shows unfavorable associations in SCLC, UVM and HNSC, but favorable associations in KIRC, SKCM and KIRP. 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 PHYH RNA expression.
This table summarizes PHYH tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 6. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PHYH. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PHYH shows lower tumor expression in COAD, KIRP, THCA, KIRC, HNSC and BRCA. The COAD box plot shows higher PHYH RNA expression in normal versus tumor tissue (log2 FC = −1.375, t-test p < 0.001).
This table shows molecular features associated with PHYH in patient tissues and cancer cell lines. In patient samples, PHYH shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, PHYH RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and BLOOD_Lymphoma.
