Q-omics provides the consensus-scored OR1A2 profile across patient tissues and cancer cell-line models. OR1A2 expression is associated with patient survival in 7 of 34 cancer types, with the highest sampling consensus in READ. Additionally, OR1A2 RNA expression shows 6,210 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight READ, and STAD as cancer lineages where OR1A2 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 OR1A2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR1A2 survival associations across molecular data types. OR1A2 RNA expression shows survival associations in the most cancer types (7), followed by mutation status (9). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OR1A2 RNA expression–survival associations across cancer types. High OR1A2 expression shows unfavorable associations in READ, ACC, ESCA, LIHC, KIRC and OV. 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 OR1A2 RNA expression.
This table shows molecular features associated with OR1A2 in patient tissues and cancer cell lines. In patient samples, OR1A2 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, OR1A2 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 SOFT_TISSUE and LARGE_INTESTINE.
