Q-omics provides the consensus-scored SELENOP profile across patient tissues and cancer cell-line models. SELENOP expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SELENOP is differentially expressed in 14, with the highest sampling consensus in COAD. Additionally, SELENOP protein abundance shows 22,115 significant protein co-abundance associations, with the highest sampling consensus in CCRCC. Together, these results highlight KIRC, COAD, and CCRCC as cancer lineages where SELENOP 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 SELENOP — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SELENOP survival associations across molecular data types. SELENOP RNA expression shows survival associations in the most cancer types (21), followed by mutation status (6) 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 SELENOP RNA expression–survival associations across cancer types. High SELENOP expression shows unfavorable associations in UCEC and STAD, but favorable associations in KIRC, COAD, LUAD and UCS. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for SELENOP RNA expression.
This table summarizes SELENOP tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 7. The strongest signals are observed in THCA for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for SELENOP. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SELENOP shows lower tumor expression in COAD, KIRP, THCA, LUAD, BLCA and LUSC. The COAD box plot shows higher SELENOP RNA expression in normal versus tumor tissue (log2 FC = −3.694, t-test p < 0.001).
This table shows molecular features associated with SELENOP in patient tissues and cancer cell lines. In patient samples, SELENOP shows the broadest associations at the RNA and protein expression levels, with CCRCC recurring as the lineage with the largest associated feature set. In cancer cell lines, SELENOP RNA and mutation anchors are most strongly linked to RNA-expression features, especially in STOMACH, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BREAST.
