Q-omics provides the consensus-scored PRAMEF26 profile across patient tissues and cancer cell-line models. PRAMEF26 expression is associated with patient survival in 8 of 34 cancer types, with the highest sampling consensus in BLCA. Additionally, PRAMEF26 RNA expression shows 5,348 significant pathway-activity associations, with the highest sampling consensus in STAD. Together, these results highlight BLCA, and STAD as cancer lineages where PRAMEF26 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 PRAMEF26 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PRAMEF26 survival associations across molecular data types. PRAMEF26 RNA expression shows survival associations in the most cancer types (8). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PRAMEF26 RNA expression–survival associations across cancer types. High PRAMEF26 expression shows unfavorable associations in BLCA, KIRC, CESC, LUAD, STAD and SKCM. The BLCA Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p = .016). Together, the overview and detailed table identify BLCA as the clearest survival context for PRAMEF26 RNA expression.
This table shows molecular features associated with PRAMEF26 in patient tissues and cancer cell lines. In patient samples, PRAMEF26 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, PRAMEF26 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in CNS and LUNG_SCLC.
