Q-omics provides the consensus-scored XRCC6 profile across patient tissues and cancer cell-line models. XRCC6 expression is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, XRCC6 is differentially expressed in 10, with the highest sampling consensus in HNSC. Additionally, XRCC6 protein abundance shows 24,205 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where XRCC6 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 XRCC6 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes XRCC6 survival associations across molecular data types. XRCC6 RNA expression shows survival associations in the most cancer types (24), followed by mutation status (4) and mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible XRCC6 RNA expression–survival associations across cancer types. High XRCC6 expression shows unfavorable associations in ACC, MESO and LIHC, but favorable associations in READ, SCLC and KIRC. 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 XRCC6 RNA expression.
This table summarizes XRCC6 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 7. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for XRCC6. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. XRCC6 shows higher tumor expression in HNSC, LIHC, LUSC, LUAD, COAD and CHOL. The HNSC box plot shows higher XRCC6 RNA expression in tumor versus normal tissue (log2 FC = +0.738, t-test p < 0.001).
This table shows molecular features associated with XRCC6 in patient tissues and cancer cell lines. In patient samples, XRCC6 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, XRCC6 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BLOOD_Leukemia.
