Q-omics provides the consensus-scored HPGD profile across patient tissues and cancer cell-line models. HPGD expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, HPGD is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, HPGD RNA expression shows 15,905 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight KIRC, HNSC, and TGCT as cancer lineages where HPGD 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 HPGD — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HPGD survival associations across molecular data types. HPGD RNA expression shows survival associations in the most cancer types (22), 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 HPGD RNA expression–survival associations across cancer types. High HPGD expression shows unfavorable associations in THCA, KIRP and PAAD, but favorable associations in KIRC, UVM and SARC. 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 HPGD RNA expression.
This table summarizes HPGD 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 7. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for HPGD. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HPGD shows lower tumor expression in HNSC, COAD, KIRP, KIRC, LUSC and LIHC. The HNSC box plot shows higher HPGD RNA expression in normal versus tumor tissue (log2 FC = −3.128, t-test p < 0.001).
This table shows molecular features associated with HPGD in patient tissues and cancer cell lines. In patient samples, HPGD 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, HPGD 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 SOFT_TISSUE and BLOOD_Leukemia.
