extra spindle pole bodies like 1, separaseGenealiases: ESP1 · SEPA
Q-omics provides the consensus-scored ESPL1 profile across patient tissues and cancer cell-line models. ESPL1 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, ESPL1 is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, ESPL1 RNA expression shows 25,796 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, COAD, and LSCC as cancer lineages where ESPL1 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 ESPL1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes ESPL1 survival associations across molecular data types. ESPL1 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (11) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible ESPL1 RNA expression–survival associations across cancer types. High ESPL1 expression shows unfavorable associations in ACC, KIRP, KICH, MESO, LIHC and KIRC. The ACC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify ACC as the clearest survival context for ESPL1 RNA expression.
This table summarizes ESPL1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 1. The strongest signals are observed in COAD for RNA and LUAD for protein.
This table ranks reproducible tumor–normal expression differences for ESPL1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. ESPL1 shows higher tumor expression in COAD, LUAD, UCEC, LUSC, BLCA and LIHC. The COAD box plot shows higher ESPL1 RNA expression in tumor versus normal tissue (log2 FC = +1.512, t-test p < 0.001).
This table shows molecular features associated with ESPL1 in patient tissues and cancer cell lines. In patient samples, ESPL1 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, ESPL1 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 OESOPHAGUS and BLOOD_Leukemia.
