Q-omics provides the consensus-scored ERCC4 profile across patient tissues and cancer cell-line models. ERCC4 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ERCC4 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, ERCC4 RNA expression shows 20,698 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, HNSC, and UVM as cancer lineages where ERCC4 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 ERCC4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ERCC4 survival associations across molecular data types. ERCC4 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (4) 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 ERCC4 RNA expression–survival associations across cancer types. High ERCC4 expression shows unfavorable associations in BLCA and LGG, but favorable associations in KIRC, HNSC, ACC and READ. 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 ERCC4 RNA expression.
This table summarizes ERCC4 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 4. The strongest signals are observed in HNSC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ERCC4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ERCC4 shows lower tumor expression in THCA and higher tumor expression in HNSC, KIRP, LIHC, COAD and BRCA. The HNSC box plot shows higher ERCC4 RNA expression in tumor versus normal tissue (log2 FC = +0.514, t-test p < 0.001).
This table shows molecular features associated with ERCC4 in patient tissues and cancer cell lines. In patient samples, ERCC4 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, ERCC4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUAD and BLOOD_Leukemia.
