HECT and RLD domain containing E3 ubiquitin protein ligase 4Genealiases: []
Q-omics provides the consensus-scored HERC4 profile across patient tissues and cancer cell-line models. HERC4 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, HERC4 is differentially expressed in 9, with the highest sampling consensus in HNSC. Additionally, HERC4 RNA expression shows 20,738 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and HNSC as cancer lineages where HERC4 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 HERC4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HERC4 survival associations across molecular data types. HERC4 RNA expression shows survival associations in the most cancer types (26), 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 HERC4 RNA expression–survival associations across cancer types. High HERC4 expression shows unfavorable associations in ACC, CESC, KIRC, LUSC, PAAD and LUAD. 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 HERC4 RNA expression.
This table summarizes HERC4 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for HERC4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HERC4 shows lower tumor expression in KICH and higher tumor expression in HNSC, LIHC, LUAD, CHOL and STAD. The HNSC box plot shows higher HERC4 RNA expression in tumor versus normal tissue (log2 FC = +0.631, t-test p < 0.001).
This table shows molecular features associated with HERC4 in patient tissues and cancer cell lines. In patient samples, HERC4 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, HERC4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in URINARY_TRACT, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and BLOOD_Leukemia.
