Q-omics provides the consensus-scored SELENOV profile across patient tissues and cancer cell-line models. SELENOV expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, SELENOV is differentially expressed in 6, with the highest sampling consensus in THCA. Additionally, SELENOV RNA expression shows 10,825 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UVM, THCA, and TGCT as cancer lineages where SELENOV 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 SELENOV — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SELENOV survival associations across molecular data types. SELENOV RNA expression shows survival associations in the most cancer types (20), 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 SELENOV RNA expression–survival associations across cancer types. High SELENOV expression shows unfavorable associations in UCEC, KIRC, ACC and MESO, but favorable associations in UVM and LUSC. The UVM Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p = .003). Together, the overview and detailed table identify UVM as the clearest survival context for SELENOV RNA expression.
This table summarizes SELENOV tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 6. The strongest signals are observed in THCA for RNA.
This table ranks reproducible tumor–normal expression differences for SELENOV. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SELENOV shows lower tumor expression in THCA, KIRC, KICH and BRCA and higher tumor expression in LUSC and UCEC. The THCA box plot shows higher SELENOV RNA expression in normal versus tumor tissue (log2 FC = −4.103, t-test p < 0.001).
This table shows molecular features associated with SELENOV in patient tissues and cancer cell lines. In patient samples, SELENOV shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, SELENOV RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BONE.
