Q-omics provides the consensus-scored CXorf51B profile across patient tissues and cancer cell-line models. CXorf51B expression is associated with patient survival in 6 of 34 cancer types, with the highest sampling consensus in STAD. Additionally, CXorf51B RNA expression shows 286 significant gene co-expression associations, with the highest sampling consensus in BLCA. Together, these results highlight STAD, and BLCA as cancer lineages where CXorf51B 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 CXorf51B — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CXorf51B survival associations across molecular data types. CXorf51B RNA expression shows survival associations in the most cancer types (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CXorf51B RNA expression–survival associations across cancer types. High CXorf51B expression shows unfavorable associations in STAD, KIRC, LUSC, BLCA, LAML and SARC. The STAD 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 STAD as the clearest survival context for CXorf51B RNA expression.
This table shows molecular features associated with CXorf51B in patient tissues and cancer cell lines. In patient samples, CXorf51B shows the broadest associations at the RNA and protein expression levels, with BLCA recurring as the lineage with the largest associated feature set. In cancer cell lines, CXorf51B 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 BLOOD_Leukemia.
