EYA transcriptional coactivator and phosphatase 3Genealiases: []
Q-omics provides the consensus-scored EYA3 profile across patient tissues and cancer cell-line models. EYA3 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, EYA3 is differentially expressed in 15, with the highest sampling consensus in KIRC. Additionally, EYA3 RNA expression shows 20,146 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, and ACC as cancer lineages where EYA3 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 EYA3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EYA3 survival associations across molecular data types. EYA3 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (3) 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 EYA3 RNA expression–survival associations across cancer types. High EYA3 expression shows unfavorable associations in ACC, MESO, LIHC and LGG, but favorable associations in KIRC and SCLC. The KIRC 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 KIRC as the clearest survival context for EYA3 RNA expression.
This table summarizes EYA3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 4. The strongest signals are observed in KIRC for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for EYA3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EYA3 shows lower tumor expression in KIRC, THCA and KICH and higher tumor expression in BLCA, HNSC and LIHC. The KIRC box plot shows higher EYA3 RNA expression in normal versus tumor tissue (log2 FC = −0.916, t-test p < 0.001).
This table shows molecular features associated with EYA3 in patient tissues and cancer cell lines. In patient samples, EYA3 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, EYA3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BLOOD_Lymphoma.
