Q-omics provides the consensus-scored OR6C1 profile across patient tissues and cancer cell-line models. OR6C1 expression is associated with patient survival in 8 of 34 cancer types, with the highest sampling consensus in ACC. Additionally, OR6C1 RNA expression shows 3,404 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight ACC, and STAD as cancer lineages where OR6C1 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 OR6C1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR6C1 survival associations across molecular data types. OR6C1 RNA expression shows survival associations in the most cancer types (8), followed by mutation status (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OR6C1 RNA expression–survival associations across cancer types. High OR6C1 expression shows unfavorable associations in ACC, SKCM, PAAD, BLCA, LAML and KIRC. The ACC 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 ACC as the clearest survival context for OR6C1 RNA expression.
This table shows molecular features associated with OR6C1 in patient tissues and cancer cell lines. In patient samples, OR6C1 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, OR6C1 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 KIDNEY and SKIN.
