Q-omics provides the consensus-scored SYCE1L profile across patient tissues and cancer cell-line models. SYCE1L expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SYCE1L is differentially expressed in 9, with the highest sampling consensus in KIRP. Additionally, SYCE1L RNA expression shows 16,347 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight ACC, KIRP, and THYM as cancer lineages where SYCE1L 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 SYCE1L — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SYCE1L survival associations across molecular data types. SYCE1L RNA expression shows survival associations in the most cancer types (20). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SYCE1L RNA expression–survival associations across cancer types. High SYCE1L expression shows unfavorable associations in ACC, KIRC and LGG, but favorable associations in UCEC, UVM and KIRP. 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 SYCE1L RNA expression.
This table summarizes SYCE1L tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9. The strongest signals are observed in THCA for RNA.
This table ranks reproducible tumor–normal expression differences for SYCE1L. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SYCE1L shows lower tumor expression in KICH and BRCA and higher tumor expression in KIRP, THCA, KIRC and LUAD. The KIRP box plot shows higher SYCE1L RNA expression in tumor versus normal tissue (log2 FC = +1.956, t-test p < 0.001).
This table shows molecular features associated with SYCE1L in patient tissues and cancer cell lines. In patient samples, SYCE1L 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, SYCE1L RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and UPPER_AERODIGESTIVE_TRACT.
