Q-omics provides the consensus-scored APEH profile across patient tissues and cancer cell-line models. APEH expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, APEH is differentially expressed in 13, with the highest sampling consensus in KIRC. Additionally, APEH protein abundance shows 22,540 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRP, KIRC, and LSCC as cancer lineages where APEH 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 APEH — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes APEH survival associations across molecular data types. APEH RNA expression shows survival associations in the most cancer types (24), followed by mutation status (5) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible APEH RNA expression–survival associations across cancer types. High APEH expression shows unfavorable associations in LAML, but favorable associations in KIRP, UVM, SCLC, CHOL and READ. The KIRP 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 KIRP as the clearest survival context for APEH RNA expression.
This table summarizes APEH 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 5. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for APEH. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. APEH shows lower tumor expression in KIRC and higher tumor expression in LIHC, COAD, BLCA, UCEC and STAD. The KIRC box plot shows higher APEH RNA expression in normal versus tumor tissue (log2 FC = −1.203, t-test p < 0.001).
This table shows molecular features associated with APEH in patient tissues and cancer cell lines. In patient samples, APEH 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, APEH 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 SOFT_TISSUE and UPPER_AERODIGESTIVE_TRACT.
