Q-omics provides the consensus-scored ECE2 profile across patient tissues and cancer cell-line models. ECE2 expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, ECE2 is differentially expressed in 17, with the highest sampling consensus in HNSC. Additionally, ECE2 RNA expression shows 19,020 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRP, HNSC, and ACC as cancer lineages where ECE2 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 ECE2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ECE2 survival associations across molecular data types. ECE2 RNA expression shows survival associations in the most cancer types (29), followed by mutation status (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ECE2 RNA expression–survival associations across cancer types. High ECE2 expression shows unfavorable associations in KIRP, MESO, BRCA, ACC, BLCA and KIRC. The KIRP 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 KIRP as the clearest survival context for ECE2 RNA expression.
This table summarizes ECE2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for ECE2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ECE2 shows higher tumor expression in HNSC, BLCA, COAD, LUAD, STAD and LUSC. The HNSC box plot shows higher ECE2 RNA expression in tumor versus normal tissue (log2 FC = +1.931, t-test p < 0.001).
This table shows molecular features associated with ECE2 in patient tissues and cancer cell lines. In patient samples, ECE2 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, ECE2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BREAST.
