chromosome segregation 1 likeGenealiases: CAS · CSE1 · XPO2
Q-omics provides the consensus-scored CSE1L profile across patient tissues and cancer cell-line models. CSE1L expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, CSE1L is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, CSE1L protein abundance shows 29,648 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight MESO, HNSC, and LSCC as cancer lineages where CSE1L 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 CSE1L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CSE1L survival associations across molecular data types. CSE1L RNA expression shows survival associations in the most cancer types (27), followed by mutation status (5) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CSE1L RNA expression–survival associations across cancer types. High CSE1L expression shows unfavorable associations in MESO, ACC, UVM, LIHC, KIRP and KICH. The MESO 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 MESO as the clearest survival context for CSE1L RNA expression.
This table summarizes CSE1L 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 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for CSE1L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CSE1L shows higher tumor expression in HNSC, BLCA, COAD, STAD, LIHC and LUSC. The HNSC box plot shows higher CSE1L RNA expression in tumor versus normal tissue (log2 FC = +1.173, t-test p < 0.001).
This table shows molecular features associated with CSE1L in patient tissues and cancer cell lines. In patient samples, CSE1L 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, CSE1L RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SOFT_TISSUE.
