HECT domain E3 ubiquitin protein ligase 2Genealiases: []
Q-omics provides the consensus-scored HECTD2 profile across patient tissues and cancer cell-line models. HECTD2 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in STAD. Among the 18 cancer types available for tumor–normal comparison, HECTD2 is differentially expressed in 12, with the highest sampling consensus in KICH. Additionally, HECTD2 RNA expression shows 20,523 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight STAD, KICH, and THYM as cancer lineages where HECTD2 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 HECTD2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes HECTD2 survival associations across molecular data types. HECTD2 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible HECTD2 RNA expression–survival associations across cancer types. High HECTD2 expression shows unfavorable associations in STAD, ACC, LIHC and UVM, but favorable associations in UCS and BRCA. The STAD Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify STAD as the clearest survival context for HECTD2 RNA expression.
This table summarizes HECTD2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 1. The strongest signals are observed in LIHC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for HECTD2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. HECTD2 shows lower tumor expression in KICH, BLCA, COAD and UCEC and higher tumor expression in LIHC and CHOL. The KICH box plot shows higher HECTD2 RNA expression in normal versus tumor tissue (log2 FC = −1.247, t-test p < 0.001).
This table shows molecular features associated with HECTD2 in patient tissues and cancer cell lines. In patient samples, HECTD2 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, HECTD2 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 BLOOD_Myeloma and BLOOD_Leukemia.
