Q-omics provides the consensus-scored EAF2 profile across patient tissues and cancer cell-line models. EAF2 expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in SKCM. Among the 18 cancer types available for tumor–normal comparison, EAF2 is differentially expressed in 16, with the highest sampling consensus in KIRP. Additionally, EAF2 RNA expression shows 18,230 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight SKCM, KIRP, and UVM as cancer lineages where EAF2 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 EAF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EAF2 survival associations across molecular data types. EAF2 RNA expression shows survival associations in the most cancer types (25), followed by mutation status (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EAF2 RNA expression–survival associations across cancer types. High EAF2 expression shows unfavorable associations in LIHC, but favorable associations in SKCM, CESC, UCEC, HNSC and LUAD. The SKCM 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 SKCM as the clearest survival context for EAF2 RNA expression.
This table summarizes EAF2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 2. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for EAF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EAF2 shows lower tumor expression in KIRP, COAD, KICH and THCA and higher tumor expression in HNSC and LIHC. The KIRP box plot shows higher EAF2 RNA expression in normal versus tumor tissue (log2 FC = −2.088, t-test p < 0.001).
This table shows molecular features associated with EAF2 in patient tissues and cancer cell lines. In patient samples, EAF2 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, EAF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in BREAST and BLOOD_Lymphoma.
