Q-omics provides the consensus-scored OR10C1 profile across patient tissues and cancer cell-line models. OR10C1 expression is associated with patient survival in 11 of 34 cancer types, with the highest sampling consensus in THCA. Additionally, OR10C1 RNA expression shows 6,547 significant gene co-expression associations, with the highest sampling consensus in UCEC. Together, these results highlight THCA, and UCEC as cancer lineages where OR10C1 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 OR10C1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR10C1 survival associations across molecular data types. OR10C1 RNA expression shows survival associations in the most cancer types (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OR10C1 RNA expression–survival associations across cancer types. High OR10C1 expression shows unfavorable associations in THCA, SARC, LGG, STAD and SKCM, but favorable associations in ESCA. The THCA Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .005). Together, the overview and detailed table identify THCA as the clearest survival context for OR10C1 RNA expression.
This table shows molecular features associated with OR10C1 in patient tissues and cancer cell lines. In patient samples, OR10C1 shows the broadest associations at the RNA and protein expression levels, with UCEC recurring as the lineage with the largest associated feature set. In cancer cell lines, OR10C1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and LARGE_INTESTINE.
