Q-omics provides the consensus-scored EDC4 profile across patient tissues and cancer cell-line models. EDC4 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, EDC4 is differentially expressed in 9, with the highest sampling consensus in KIRP. Additionally, EDC4 protein abundance shows 32,799 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight SCLC, KIRP, and LSCC as cancer lineages where EDC4 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 EDC4 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EDC4 survival associations across molecular data types. EDC4 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) and mass-spec protein abundance (10). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EDC4 RNA expression–survival associations across cancer types. High EDC4 expression shows unfavorable associations in ACC, LUSC and LIHC, but favorable associations in SCLC, UCEC and UCS. The SCLC 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 SCLC as the clearest survival context for EDC4 RNA expression.
This table summarizes EDC4 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 12. The strongest signals are observed in KIRP for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EDC4. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EDC4 shows higher tumor expression in KIRP, HNSC, LIHC, STAD, CHOL and KIRC. The KIRP box plot shows higher EDC4 RNA expression in tumor versus normal tissue (log2 FC = +1.274, t-test p < 0.001).
This table shows molecular features associated with EDC4 in patient tissues and cancer cell lines. In patient samples, EDC4 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, EDC4 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in SKIN and BLOOD_Leukemia.
