Q-omics provides the consensus-scored OR6K6 profile across patient tissues and cancer cell-line models. OR6K6 expression is associated with patient survival in 14 of 34 cancer types, with the highest sampling consensus in UCS. Additionally, OR6K6 RNA expression shows 5,048 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight UCS, and STAD as cancer lineages where OR6K6 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 OR6K6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR6K6 survival associations across molecular data types. OR6K6 RNA expression shows survival associations in the most cancer types (14), followed by mutation status (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OR6K6 RNA expression–survival associations across cancer types. High OR6K6 expression shows unfavorable associations in UCS, THCA, CHOL, LUAD, ESCA and GBM. The UCS Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify UCS as the clearest survival context for OR6K6 RNA expression.
This table shows molecular features associated with OR6K6 in patient tissues and cancer cell lines. In patient samples, OR6K6 shows the broadest associations at the RNA and protein expression levels, with STAD recurring as the lineage with the largest associated feature set. In cancer cell lines, OR6K6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in OVARY, while CRISPR and shRNA rows add functional-dependency signals in LARGE_INTESTINE and CNS.
