Q-omics provides the consensus-scored USP17L8 profile across patient tissues and cancer cell-line models. USP17L8 expression is associated with patient survival in 5 of 34 cancer types, with the highest sampling consensus in ESCA. Additionally, USP17L8 RNA expression shows 2,039 significant gene co-expression associations, with the highest sampling consensus in COAD. Together, these results highlight ESCA, and COAD as cancer lineages where USP17L8 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 USP17L8 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes USP17L8 survival associations across molecular data types. USP17L8 RNA expression shows survival associations in the most cancer types (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible USP17L8 RNA expression–survival associations across cancer types. High USP17L8 expression shows unfavorable associations in ESCA, LUAD, THCA, SKCM and KIRC. The ESCA 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 ESCA as the clearest survival context for USP17L8 RNA expression.
This table shows molecular features associated with USP17L8 in patient tissues and cancer cell lines. In patient samples, USP17L8 shows the broadest associations at the RNA and protein expression levels, with COAD recurring as the lineage with the largest associated feature set. In cancer cell lines, USP17L8 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia.
