Q-omics provides the consensus-scored HERPUD2 profile across patient tissues and cancer cell-line models. HERPUD2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in CESC. Among the 18 cancer types available for tumor–normal comparison, HERPUD2 is differentially expressed in 11, with the highest sampling consensus in HNSC. Additionally, HERPUD2 RNA expression shows 19,922 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight CESC, HNSC, and ACC as cancer lineages where HERPUD2 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 HERPUD2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HERPUD2 survival associations across molecular data types. HERPUD2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (7) 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 HERPUD2 RNA expression–survival associations across cancer types. High HERPUD2 expression shows unfavorable associations in CESC, BLCA, ACC, HNSC and KIRP, but favorable associations in KIRC. The CESC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .005). Together, the overview and detailed table identify CESC as the clearest survival context for HERPUD2 RNA expression.
This table summarizes HERPUD2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 7. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for HERPUD2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HERPUD2 shows lower tumor expression in THCA and UCEC and higher tumor expression in HNSC, KIRC, LIHC and KIRP. The HNSC box plot shows higher HERPUD2 RNA expression in tumor versus normal tissue (log2 FC = +0.735, t-test p < 0.001).
This table shows molecular features associated with HERPUD2 in patient tissues and cancer cell lines. In patient samples, HERPUD2 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, HERPUD2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and BLOOD_Leukemia.
