Q-omics provides the consensus-scored EPM2AIP1 profile across patient tissues and cancer cell-line models. EPM2AIP1 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, EPM2AIP1 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, EPM2AIP1 RNA expression shows 21,755 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight ACC, KIRC, and THYM as cancer lineages where EPM2AIP1 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 EPM2AIP1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EPM2AIP1 survival associations across molecular data types. EPM2AIP1 RNA expression shows survival associations in the most cancer types (27), 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 EPM2AIP1 RNA expression–survival associations across cancer types. High EPM2AIP1 expression shows unfavorable associations in ACC and KICH, but favorable associations in BRCA, SKCM, MESO and UCS. The ACC 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 ACC as the clearest survival context for EPM2AIP1 RNA expression.
This table summarizes EPM2AIP1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EPM2AIP1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EPM2AIP1 shows lower tumor expression in KIRC, UCEC, THCA, LUSC and LUAD and higher tumor expression in LIHC. The KIRC box plot shows higher EPM2AIP1 RNA expression in normal versus tumor tissue (log2 FC = −0.757, t-test p < 0.001).
This table shows molecular features associated with EPM2AIP1 in patient tissues and cancer cell lines. In patient samples, EPM2AIP1 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, EPM2AIP1 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 CNS and BLOOD_Leukemia.
