Q-omics provides the consensus-scored XRCC2 profile across patient tissues and cancer cell-line models. XRCC2 expression is associated with patient survival in 29 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, XRCC2 is differentially expressed in 17, with the highest sampling consensus in BLCA. Additionally, XRCC2 RNA expression shows 25,575 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight ACC, BLCA, and LSCC as cancer lineages where XRCC2 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 XRCC2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes XRCC2 survival associations across molecular data types. XRCC2 RNA expression shows survival associations in the most cancer types (29). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible XRCC2 RNA expression–survival associations across cancer types. High XRCC2 expression shows unfavorable associations in ACC, KIRP, MESO, KIRC, LIHC and KICH. The ACC 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 ACC as the clearest survival context for XRCC2 RNA expression.
This table summarizes XRCC2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for XRCC2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. XRCC2 shows higher tumor expression in BLCA, HNSC, LUAD, COAD, KIRP and KIRC. The BLCA box plot shows higher XRCC2 RNA expression in tumor versus normal tissue (log2 FC = +2.035, t-test p < 0.001).
This table shows molecular features associated with XRCC2 in patient tissues and cancer cell lines. In patient samples, XRCC2 shows the broadest associations at the RNA and protein expression levels, with LSCC recurring as the lineage with the largest associated feature set. In cancer cell lines, XRCC2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BONE, while CRISPR and shRNA rows add functional-dependency signals in LUNG_SCLC and BLOOD_Leukemia.
