Q-omics provides the consensus-scored CHEK2 profile across patient tissues and cancer cell-line models. CHEK2 expression is associated with patient survival in 26 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, CHEK2 is differentially expressed in 17, with the highest sampling consensus in KIRC. Additionally, CHEK2 protein abundance shows 25,733 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight KIRC, and LSCC as cancer lineages where CHEK2 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 CHEK2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CHEK2 survival associations across molecular data types. CHEK2 RNA expression shows survival associations in the most cancer types (26), followed by mutation status (8) 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 CHEK2 RNA expression–survival associations across cancer types. High CHEK2 expression shows unfavorable associations in KIRC, ACC, KICH and UVM, but favorable associations in LUSC and READ. 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 CHEK2 RNA expression.
This table summarizes CHEK2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 17, while mass-spec protein shows differences in 7. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for CHEK2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CHEK2 shows higher tumor expression in KIRC, KIRP, HNSC, BLCA, STAD and LUSC. The KIRC box plot shows higher CHEK2 RNA expression in tumor versus normal tissue (log2 FC = +1.066, t-test p < 0.001).
This table shows molecular features associated with CHEK2 in patient tissues and cancer cell lines. In patient samples, CHEK2 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, CHEK2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Lymphoma and BONE.
