Q-omics provides the consensus-scored OR10K1 profile across patient tissues and cancer cell-line models. OR10K1 expression is associated with patient survival in 7 of 34 cancer types, with the highest sampling consensus in MESO. Additionally, OR10K1 RNA expression shows 9,135 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight MESO, and THYM as cancer lineages where OR10K1 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 OR10K1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR10K1 survival associations across molecular data types. OR10K1 RNA expression shows survival associations in the most cancer types (7), followed by mutation status (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OR10K1 RNA expression–survival associations across cancer types. High OR10K1 expression shows unfavorable associations in MESO, BLCA, SKCM, THCA, LGG and KIRC. The MESO 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 MESO as the clearest survival context for OR10K1 RNA expression.
This table shows molecular features associated with OR10K1 in patient tissues and cancer cell lines. In patient samples, OR10K1 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, OR10K1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_NSCLC_LUAD, while CRISPR and shRNA rows add functional-dependency signals in LUNG_NSCLC_LUSC and BLOOD_Leukemia.
