Q-omics provides the consensus-scored USP17L3 profile across patient tissues and cancer cell-line models. USP17L3 expression is associated with patient survival in 8 of 34 cancer types, with the highest sampling consensus in STAD. Additionally, USP17L3 RNA expression shows 2,629 significant gene co-expression associations, with the highest sampling consensus in LUAD. Together, these results highlight STAD, and LUAD as cancer lineages where USP17L3 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 USP17L3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes USP17L3 survival associations across molecular data types. USP17L3 RNA expression shows survival associations in the most cancer types (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible USP17L3 RNA expression–survival associations across cancer types. High USP17L3 expression shows unfavorable associations in STAD, KICH, READ, OV, KIRC and LUSC. The STAD 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 STAD as the clearest survival context for USP17L3 RNA expression.
This table shows molecular features associated with USP17L3 in patient tissues and cancer cell lines. In patient samples, USP17L3 shows the broadest associations at the RNA and protein expression levels, with LUAD recurring as the lineage with the largest associated feature set. In cancer cell lines, USP17L3 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 SOFT_TISSUE and UPPER_AERODIGESTIVE_TRACT.
