Q-omics provides the consensus-scored OR2G2 profile across patient tissues and cancer cell-line models. OR2G2 expression is associated with patient survival in 11 of 34 cancer types, with the highest sampling consensus in SKCM. Additionally, OR2G2 RNA expression shows 6,289 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight SKCM, and STAD as cancer lineages where OR2G2 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 OR2G2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes OR2G2 survival associations across molecular data types. OR2G2 RNA expression shows survival associations in the most cancer types (11), followed by mutation status (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible OR2G2 RNA expression–survival associations across cancer types. High OR2G2 expression shows unfavorable associations in SKCM, COAD, CESC, LUSC and STAD, but favorable associations in ESCA. The SKCM 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 SKCM as the clearest survival context for OR2G2 RNA expression.
This table shows molecular features associated with OR2G2 in patient tissues and cancer cell lines. In patient samples, OR2G2 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, OR2G2 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 SKIN and BLOOD_Myeloma.
