Q-omics provides the consensus-scored DEFB105A profile across patient tissues and cancer cell-line models. DEFB105A expression is associated with patient survival in 7 of 34 cancer types, with the highest sampling consensus in CESC. Additionally, DEFB105A RNA expression shows 5,375 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight CESC, and STAD as cancer lineages where DEFB105A 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 DEFB105A — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes DEFB105A survival associations across molecular data types. DEFB105A RNA expression shows survival associations in the most cancer types (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible DEFB105A RNA expression–survival associations across cancer types. High DEFB105A expression shows unfavorable associations in CESC, KIRP, STAD, PAAD, TGCT 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 = .007). Together, the overview and detailed table identify CESC as the clearest survival context for DEFB105A RNA expression.
This table shows molecular features associated with DEFB105A in patient tissues and cancer cell lines. In patient samples, DEFB105A shows the broadest associations at the RNA and protein expression levels, with STAD recurring as the lineage with the largest associated feature set. In cancer cell lines, DEFB105A RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC.
