Q-omics provides the consensus-scored RAD50 profile across patient tissues and cancer cell-line models. RAD50 expression is associated with patient survival in 23 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, RAD50 is differentially expressed in 10, with the highest sampling consensus in KIRC. Additionally, RAD50 protein abundance shows 20,880 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight KIRC, and GBM as cancer lineages where RAD50 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 RAD50 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RAD50 survival associations across molecular data types. RAD50 RNA expression shows survival associations in the most cancer types (23), followed by mutation status (5) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible RAD50 RNA expression–survival associations across cancer types. High RAD50 expression shows unfavorable associations in CESC, UVM, HNSC and PAAD, but favorable associations in KIRC and READ. 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 RAD50 RNA expression.
This table summarizes RAD50 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for RAD50. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RAD50 shows lower tumor expression in THCA and higher tumor expression in KIRC, LIHC, KIRP, HNSC and BRCA. The KIRC box plot shows higher RAD50 RNA expression in tumor versus normal tissue (log2 FC = +0.490, t-test p < 0.001).
This table shows molecular features associated with RAD50 in patient tissues and cancer cell lines. In patient samples, RAD50 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, RAD50 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and LARGE_INTESTINE.
