Q-omics provides the consensus-scored OR10G9 profile across patient tissues and cancer cell-line models. OR10G9 expression is associated with patient survival in 8 of 34 cancer types, with the highest sampling consensus in KIRC. Additionally, OR10G9 RNA expression shows 6,058 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KIRC, and PDAC as cancer lineages where OR10G9 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 OR10G9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR10G9 survival associations across molecular data types. OR10G9 RNA expression shows survival associations in the most cancer types (8), followed by mutation status (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OR10G9 RNA expression–survival associations across cancer types. High OR10G9 expression shows unfavorable associations in KIRC, STAD, HNSC, KIRP, ACC and SKCM. The KIRC 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 KIRC as the clearest survival context for OR10G9 RNA expression.
This table shows molecular features associated with OR10G9 in patient tissues and cancer cell lines. In patient samples, OR10G9 shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, OR10G9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
