Q-omics provides the consensus-scored EYS profile across patient tissues and cancer cell-line models. EYS expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, EYS is differentially expressed in 13, with the highest sampling consensus in THCA. Additionally, EYS RNA expression shows 20,815 significant gene co-expression associations, with the highest sampling consensus in KIRP. Together, these results highlight SCLC, THCA, and KIRP as cancer lineages where EYS 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 EYS — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EYS survival associations across molecular data types. EYS RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EYS RNA expression–survival associations across cancer types. High EYS expression shows unfavorable associations in KIRP, ACC and STAD, but favorable associations in SCLC, KIRC and LGG. The SCLC 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 SCLC as the clearest survival context for EYS RNA expression.
This table summarizes EYS tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13. The strongest signals are observed in THCA for RNA.
This table ranks reproducible tumor–normal expression differences for EYS. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EYS shows lower tumor expression in THCA and BRCA and higher tumor expression in HNSC, LUAD, LIHC and STAD. The THCA box plot shows higher EYS RNA expression in normal versus tumor tissue (log2 FC = −0.088, t-test p < 0.001).
This table shows molecular features associated with EYS in patient tissues and cancer cell lines. In patient samples, EYS shows the broadest associations at the RNA and protein expression levels, with KIRP recurring as the lineage with the largest associated feature set. In cancer cell lines, EYS RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
