Q-omics provides the consensus-scored ACHE profile across patient tissues and cancer cell-line models. ACHE expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, ACHE is differentially expressed in 14, with the highest sampling consensus in KIRP. Additionally, ACHE RNA expression shows 15,106 significant gene co-expression associations, with the highest sampling consensus in ESCA. Together, these results highlight KIRC, KIRP, and ESCA as cancer lineages where ACHE 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 ACHE — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ACHE survival associations across molecular data types. ACHE RNA expression shows survival associations in the most cancer types (23), followed by mutation status (4) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ACHE RNA expression–survival associations across cancer types. High ACHE expression shows unfavorable associations in KIRC, UVM, MESO and ACC, but favorable associations in SKCM and SCLC. The KIRC 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 KIRC as the clearest survival context for ACHE RNA expression.
This table summarizes ACHE tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 5. The strongest signals are observed in KIRP for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for ACHE. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ACHE shows lower tumor expression in KICH, UCEC, THCA, HNSC and BRCA and higher tumor expression in KIRP. The KIRP box plot shows higher ACHE RNA expression in tumor versus normal tissue (log2 FC = +1.564, t-test p < 0.001).
This table shows molecular features associated with ACHE in patient tissues and cancer cell lines. In patient samples, ACHE shows the broadest associations at the RNA and protein expression levels, with ESCA recurring as the lineage with the largest associated feature set. In cancer cell lines, ACHE RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in OESOPHAGUS and LARGE_INTESTINE.
