Q-omics provides the consensus-scored CETN1 profile across patient tissues and cancer cell-line models. CETN1 expression is associated with patient survival in 14 of 34 cancer types, with the highest sampling consensus in UVM. Additionally, CETN1 RNA expression shows 7,703 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight UVM, and TGCT as cancer lineages where CETN1 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 CETN1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CETN1 survival associations across molecular data types. CETN1 RNA expression shows survival associations in the most cancer types (14), followed by mutation status (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CETN1 RNA expression–survival associations across cancer types. High CETN1 expression shows unfavorable associations in UVM, LIHC, MESO, LUAD and COAD, but favorable associations in UCS. The UVM 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 UVM as the clearest survival context for CETN1 RNA expression.
This table shows molecular features associated with CETN1 in patient tissues and cancer cell lines. In patient samples, CETN1 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, CETN1 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Myeloma and LUNG_SCLC.
