Q-omics provides the consensus-scored OR4K15 profile across patient tissues and cancer cell-line models. OR4K15 expression is associated with patient survival in 7 of 34 cancer types, with the highest sampling consensus in SCLC. Additionally, OR4K15 RNA expression shows 5,614 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight SCLC, and STAD as cancer lineages where OR4K15 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 OR4K15 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR4K15 survival associations across molecular data types. OR4K15 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 OR4K15 RNA expression–survival associations across cancer types. High OR4K15 expression shows unfavorable associations in SCLC, LUAD, KIRP, CESC, SARC and UCEC. The SCLC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .016). Together, the overview and detailed table identify SCLC as the clearest survival context for OR4K15 RNA expression.
This table shows molecular features associated with OR4K15 in patient tissues and cancer cell lines. In patient samples, OR4K15 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, OR4K15 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in STOMACH and BREAST.
