Q-omics provides the consensus-scored DEFB133 profile across patient tissues and cancer cell-line models. DEFB133 expression is associated with patient survival in 12 of 34 cancer types, with the highest sampling consensus in CESC. Additionally, DEFB133 RNA expression shows 6,226 significant gene co-expression associations, with the highest sampling consensus in LIHC. Together, these results highlight CESC, and LIHC as cancer lineages where DEFB133 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 DEFB133 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DEFB133 survival associations across molecular data types. DEFB133 RNA expression shows survival associations in the most cancer types (12), followed by mutation status (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DEFB133 RNA expression–survival associations across cancer types. High DEFB133 expression shows unfavorable associations in CESC, KICH, LIHC, BRCA, STAD and BLCA. The CESC 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 CESC as the clearest survival context for DEFB133 RNA expression.
This table shows molecular features associated with DEFB133 in patient tissues and cancer cell lines. In patient samples, DEFB133 shows the broadest associations at the RNA and protein expression levels, with LIHC recurring as the lineage with the largest associated feature set. In cancer cell lines, DEFB133 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 CNS.
