Q-omics provides the consensus-scored PRAMEF13 profile across patient tissues and cancer cell-line models. PRAMEF13 expression is associated with patient survival in 11 of 34 cancer types, with the highest sampling consensus in HNSC. Additionally, PRAMEF13 RNA expression shows 7,150 significant gene co-expression associations, with the highest sampling consensus in THYM. Together, these results highlight HNSC, and THYM as cancer lineages where PRAMEF13 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 PRAMEF13 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PRAMEF13 survival associations across molecular data types. PRAMEF13 RNA expression shows survival associations in the most cancer types (11). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PRAMEF13 RNA expression–survival associations across cancer types. High PRAMEF13 expression shows unfavorable associations in HNSC, LIHC, UCS, KIRP, UCEC and LUAD. The HNSC 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 HNSC as the clearest survival context for PRAMEF13 RNA expression.
This table shows molecular features associated with PRAMEF13 in patient tissues and cancer cell lines. In patient samples, PRAMEF13 shows the broadest associations at the RNA and protein expression levels, with THYM recurring as the lineage with the largest associated feature set. In cancer cell lines, PRAMEF13 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in UPPER_AERODIGESTIVE_TRACT, while CRISPR and shRNA rows add functional-dependency signals in CNS.
