Q-omics provides the consensus-scored OR4L1 profile across patient tissues and cancer cell-line models. OR4L1 expression is associated with patient survival in 9 of 34 cancer types, with the highest sampling consensus in THYM. Additionally, OR4L1 RNA expression shows 6,383 significant gene co-expression associations, with the highest sampling consensus in COAD. Together, these results highlight THYM, and COAD as cancer lineages where OR4L1 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 OR4L1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR4L1 survival associations across molecular data types. OR4L1 RNA expression shows survival associations in the most cancer types (9), 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 OR4L1 RNA expression–survival associations across cancer types. High OR4L1 expression shows unfavorable associations in THYM, KIRP, CESC, LUSC, GBM and UVM. The THYM 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 THYM as the clearest survival context for OR4L1 RNA expression.
This table shows molecular features associated with OR4L1 in patient tissues and cancer cell lines. In patient samples, OR4L1 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, OR4L1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Lymphoma, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and LARGE_INTESTINE.
