Q-omics provides the consensus-scored OR9G1 profile across patient tissues and cancer cell-line models. OR9G1 expression is associated with patient survival in 14 of 34 cancer types, with the highest sampling consensus in READ. Additionally, OR9G1 RNA expression shows 5,825 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight READ, and STAD as cancer lineages where OR9G1 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 OR9G1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR9G1 survival associations across molecular data types. OR9G1 RNA expression shows survival associations in the most cancer types (14), followed by mutation status (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OR9G1 RNA expression–survival associations across cancer types. High OR9G1 expression shows unfavorable associations in READ, LUSC, KIRP, COAD, CHOL and SKCM. The READ 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 READ as the clearest survival context for OR9G1 RNA expression.
This table shows molecular features associated with OR9G1 in patient tissues and cancer cell lines. In patient samples, OR9G1 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, OR9G1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LARGE_INTESTINE, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and LUNG_SCLC.
