Q-omics provides the consensus-scored OR5D16 profile across patient tissues and cancer cell-line models. OR5D16 expression is associated with patient survival in 7 of 34 cancer types, with the highest sampling consensus in ESCA. Additionally, OR5D16 RNA expression shows 6,301 significant gene co-expression associations, with the highest sampling consensus in COAD. Together, these results highlight ESCA, and COAD as cancer lineages where OR5D16 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 OR5D16 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR5D16 survival associations across molecular data types. OR5D16 RNA expression shows survival associations in the most cancer types (7), 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 OR5D16 RNA expression–survival associations across cancer types. High OR5D16 expression shows unfavorable associations in ESCA, STAD, BLCA, LAML, SARC and SKCM. The ESCA 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 ESCA as the clearest survival context for OR5D16 RNA expression.
This table shows molecular features associated with OR5D16 in patient tissues and cancer cell lines. In patient samples, OR5D16 shows the broadest associations at the RNA and protein expression levels, with COAD recurring as the lineage with the largest associated feature set. In cancer cell lines, OR5D16 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LUNG_NSCLC_LUAD.
