Q-omics provides the consensus-scored EPS15L1 profile across patient tissues and cancer cell-line models. EPS15L1 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KICH. Among the 18 cancer types available for tumor–normal comparison, EPS15L1 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, EPS15L1 protein abundance shows 26,415 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KICH, HNSC, and PDAC as cancer lineages where EPS15L1 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 EPS15L1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EPS15L1 survival associations across molecular data types. EPS15L1 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (3) and mass-spec protein abundance (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EPS15L1 RNA expression–survival associations across cancer types. High EPS15L1 expression shows unfavorable associations in KICH, MESO and ACC, but favorable associations in BRCA, KIRC and HNSC. The KICH Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .001). Together, the overview and detailed table identify KICH as the clearest survival context for EPS15L1 RNA expression.
This table summarizes EPS15L1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for EPS15L1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EPS15L1 shows lower tumor expression in THCA and higher tumor expression in HNSC, KIRP, LIHC, KIRC and KICH. The HNSC box plot shows higher EPS15L1 RNA expression in tumor versus normal tissue (log2 FC = +0.506, t-test p < 0.001).
This table shows molecular features associated with EPS15L1 in patient tissues and cancer cell lines. In patient samples, EPS15L1 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, EPS15L1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and BLOOD_Lymphoma.
