Q-omics provides the consensus-scored RADX profile across patient tissues and cancer cell-line models. RADX expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, RADX is differentially expressed in 15, with the highest sampling consensus in KICH. Additionally, RADX RNA expression shows 19,513 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, KICH, and UVM as cancer lineages where RADX 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 RADX — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RADX survival associations across molecular data types. RADX RNA expression shows survival associations in the most cancer types (29), followed by mutation status (5) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RADX RNA expression–survival associations across cancer types. High RADX expression shows unfavorable associations in KIRP, UCEC and LIHC, but favorable associations in KIRC, PAAD and LGG. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for RADX RNA expression.
This table summarizes RADX tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 15, while mass-spec protein shows differences in 2. The strongest signals are observed in THCA for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for RADX. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RADX shows lower tumor expression in KICH, LUAD, THCA and UCEC and higher tumor expression in HNSC and LIHC. The KICH box plot shows higher RADX RNA expression in normal versus tumor tissue (log2 FC = −2.536, t-test p < 0.001).
This table shows molecular features associated with RADX in patient tissues and cancer cell lines. In patient samples, RADX shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, RADX RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in LIVER and LARGE_INTESTINE.
