Q-omics provides the consensus-scored SEL1L profile across patient tissues and cancer cell-line models. SEL1L expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in BLCA. Among the 18 cancer types available for tumor–normal comparison, SEL1L is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, SEL1L RNA expression shows 20,232 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight BLCA, HNSC, and THYM as cancer lineages where SEL1L 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 SEL1L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SEL1L survival associations across molecular data types. SEL1L RNA expression shows survival associations in the most cancer types (21), followed by mutation status (4) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SEL1L RNA expression–survival associations across cancer types. High SEL1L expression shows unfavorable associations in BLCA, LUSC and UVM, but favorable associations in SKCM, KIRC and BRCA. The BLCA 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 BLCA as the clearest survival context for SEL1L RNA expression.
This table summarizes SEL1L tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for SEL1L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SEL1L shows lower tumor expression in THCA, COAD and READ and higher tumor expression in HNSC, BRCA and LIHC. The HNSC box plot shows higher SEL1L RNA expression in tumor versus normal tissue (log2 FC = +1.556, t-test p < 0.001).
This table shows molecular features associated with SEL1L in patient tissues and cancer cell lines. In patient samples, SEL1L shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, SEL1L RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OESOPHAGUS, while CRISPR and shRNA rows add functional-dependency signals in CNS and UPPER_AERODIGESTIVE_TRACT.
