Q-omics provides the consensus-scored PGGHG profile across patient tissues and cancer cell-line models. PGGHG expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, PGGHG is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, PGGHG RNA expression shows 16,540 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight UVM, and COAD as cancer lineages where PGGHG 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 PGGHG — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PGGHG survival associations across molecular data types. PGGHG RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PGGHG RNA expression–survival associations across cancer types. High PGGHG expression shows unfavorable associations in UVM, KIRC, MESO and ACC, but favorable associations in SKCM and BLCA. The UVM 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 UVM as the clearest survival context for PGGHG RNA expression.
This table summarizes PGGHG 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 2. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PGGHG. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PGGHG shows higher tumor expression in COAD, KIRC, HNSC, STAD, BLCA and LIHC. The COAD box plot shows higher PGGHG RNA expression in tumor versus normal tissue (log2 FC = +2.991, t-test p < 0.001).
This table shows molecular features associated with PGGHG in patient tissues and cancer cell lines. In patient samples, PGGHG 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, PGGHG RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
