Q-omics provides the consensus-scored SELENOW profile across patient tissues and cancer cell-line models. SELENOW expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, SELENOW is differentially expressed in 12, with the highest sampling consensus in BLCA. Additionally, SELENOW protein abundance shows 20,786 significant protein co-abundance associations, with the highest sampling consensus in UCEC. Together, these results highlight UVM, BLCA, and UCEC as cancer lineages where SELENOW 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 SELENOW — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SELENOW survival associations across molecular data types. SELENOW RNA expression shows survival associations in the most cancer types (21), followed by mutation status (2) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SELENOW RNA expression–survival associations across cancer types. High SELENOW expression shows unfavorable associations in UVM, MESO, HNSC, BRCA, LIHC and LAML. The UVM Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .002). Together, the overview and detailed table identify UVM as the clearest survival context for SELENOW RNA expression.
This table summarizes SELENOW tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 6. The strongest signals are observed in BLCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SELENOW. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SELENOW shows lower tumor expression in BLCA, LUSC, UCEC and KICH and higher tumor expression in KIRC and PAAD. The BLCA box plot shows higher SELENOW RNA expression in normal versus tumor tissue (log2 FC = −1.625, t-test p < 0.001).
This table shows molecular features associated with SELENOW in patient tissues and cancer cell lines. In patient samples, SELENOW shows the broadest associations at the RNA and protein expression levels, with UCEC recurring as the lineage with the largest associated feature set. In cancer cell lines, SELENOW 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 BLOOD_Lymphoma.
