Q-omics provides the consensus-scored ERCC6L profile across patient tissues and cancer cell-line models. ERCC6L expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, ERCC6L is differentially expressed in 17, with the highest sampling consensus in HNSC. Additionally, ERCC6L protein abundance shows 24,115 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight MESO, HNSC, and LSCC as cancer lineages where ERCC6L 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 ERCC6L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ERCC6L survival associations across molecular data types. ERCC6L RNA expression shows survival associations in the most cancer types (27), followed by mutation status (8) 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 ERCC6L RNA expression–survival associations across cancer types. High ERCC6L expression shows unfavorable associations in MESO, KIRP, ACC, KICH, LIHC and UVM. The MESO 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 MESO as the clearest survival context for ERCC6L RNA expression.
This table summarizes ERCC6L tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for ERCC6L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ERCC6L shows higher tumor expression in HNSC, BLCA, LUAD, COAD, KIRP and KIRC. The HNSC box plot shows higher ERCC6L RNA expression in tumor versus normal tissue (log2 FC = +1.501, t-test p < 0.001).
This table shows molecular features associated with ERCC6L in patient tissues and cancer cell lines. In patient samples, ERCC6L shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ERCC6L 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 SKIN and BLOOD_Leukemia.
