Q-omics provides the consensus-scored SELENOM profile across patient tissues and cancer cell-line models. SELENOM expression is associated with patient survival in 25 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, SELENOM is differentially expressed in 11, with the highest sampling consensus in BLCA. Additionally, SELENOM protein abundance shows 26,632 significant protein co-abundance associations, with the highest sampling consensus in PDAC. Together, these results highlight KIRC, BLCA, and PDAC as cancer lineages where SELENOM 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 SELENOM — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SELENOM survival associations across molecular data types. SELENOM RNA expression shows survival associations in the most cancer types (25), followed by mutation status (3) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SELENOM RNA expression–survival associations across cancer types. High SELENOM expression shows unfavorable associations in KIRC, UVM, KIRP, LGG and UCS, but favorable associations in SKCM. The KIRC 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 KIRC as the clearest survival context for SELENOM RNA expression.
This table summarizes SELENOM tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 5. The strongest signals are observed in BLCA for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for SELENOM. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SELENOM shows lower tumor expression in BLCA, KIRP, THCA and UCEC and higher tumor expression in LIHC and HNSC. The BLCA box plot shows higher SELENOM RNA expression in normal versus tumor tissue (log2 FC = −2.995, t-test p < 0.001).
This table shows molecular features associated with SELENOM in patient tissues and cancer cell lines. In patient samples, SELENOM shows the broadest associations at the RNA and protein expression levels, with PDAC recurring as the lineage with the largest associated feature set. In cancer cell lines, SELENOM 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 PANCREAS and BONE.
