Q-omics provides the consensus-scored EPS8L2 profile across patient tissues and cancer cell-line models. EPS8L2 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, EPS8L2 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, EPS8L2 RNA expression shows 17,839 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UVM, KICH, and TGCT as cancer lineages where EPS8L2 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 EPS8L2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EPS8L2 survival associations across molecular data types. EPS8L2 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EPS8L2 RNA expression–survival associations across cancer types. High EPS8L2 expression shows unfavorable associations in LGG, LIHC, ACC and GBM, but favorable associations in UVM and KIRP. The UVM Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .001). Together, the overview and detailed table identify UVM as the clearest survival context for EPS8L2 RNA expression.
This table summarizes EPS8L2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 6. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EPS8L2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EPS8L2 shows lower tumor expression in KICH and HNSC and higher tumor expression in BLCA, LIHC, BRCA and CHOL. The KICH box plot shows higher EPS8L2 RNA expression in normal versus tumor tissue (log2 FC = −2.223, t-test p < 0.001).
This table shows molecular features associated with EPS8L2 in patient tissues and cancer cell lines. In patient samples, EPS8L2 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, EPS8L2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and LUNG_NSCLC_LUAD.
