Q-omics provides the consensus-scored OR6Q1 profile across patient tissues and cancer cell-line models. OR6Q1 expression is associated with patient survival in 6 of 34 cancer types, with the highest sampling consensus in LUAD. Additionally, OR6Q1 RNA expression shows 5,499 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight LUAD, and STAD as cancer lineages where OR6Q1 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 OR6Q1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR6Q1 survival associations across molecular data types. OR6Q1 RNA expression shows survival associations in the most cancer types (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OR6Q1 RNA expression–survival associations across cancer types. High OR6Q1 expression shows unfavorable associations in LUAD, LUSC, BRCA, OV and THCA, but favorable associations in ESCA. The LUAD 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 LUAD as the clearest survival context for OR6Q1 RNA expression.
This table shows molecular features associated with OR6Q1 in patient tissues and cancer cell lines. In patient samples, OR6Q1 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, OR6Q1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in PANCREAS and LARGE_INTESTINE.
