Q-omics provides the consensus-scored RAD52 profile across patient tissues and cancer cell-line models. RAD52 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, RAD52 is differentially expressed in 11, with the highest sampling consensus in LIHC. Additionally, RAD52 RNA expression shows 20,572 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight KIRC, LIHC, and UVM as cancer lineages where RAD52 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 RAD52 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes RAD52 survival associations across molecular data types. RAD52 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (1) 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 RAD52 RNA expression–survival associations across cancer types. High RAD52 expression shows unfavorable associations in KIRC, CESC, COAD, ACC and KICH, but favorable associations in SCLC. The KIRC 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 KIRC as the clearest survival context for RAD52 RNA expression.
This table summarizes RAD52 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 3. The strongest signals are observed in LIHC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for RAD52. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. RAD52 shows lower tumor expression in BRCA and KICH and higher tumor expression in LIHC, HNSC, CHOL and BLCA. The LIHC box plot shows higher RAD52 RNA expression in tumor versus normal tissue (log2 FC = +0.773, t-test p < 0.001).
This table shows molecular features associated with RAD52 in patient tissues and cancer cell lines. In patient samples, RAD52 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, RAD52 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and SOFT_TISSUE.
