Q-omics provides the consensus-scored CHEK1 profile across patient tissues and cancer cell-line models. CHEK1 expression is associated with patient survival in 30 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, CHEK1 is differentially expressed in 16, with the highest sampling consensus in HNSC. Additionally, CHEK1 protein abundance shows 23,942 significant protein co-abundance associations, with the highest sampling consensus in LSCC. Together, these results highlight MESO, HNSC, and LSCC as cancer lineages where CHEK1 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 CHEK1 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CHEK1 survival associations across molecular data types. CHEK1 RNA expression shows survival associations in the most cancer types (30), followed by mutation status (7) 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 CHEK1 RNA expression–survival associations across cancer types. High CHEK1 expression shows unfavorable associations in MESO, ACC, LIHC, KICH and LGG, but favorable associations in UCS. The MESO 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 MESO as the clearest survival context for CHEK1 RNA expression.
This table summarizes CHEK1 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 16, while mass-spec protein shows differences in 6. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for CHEK1. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CHEK1 shows higher tumor expression in HNSC, KIRC, BLCA, KIRP, LUAD and STAD. The HNSC box plot shows higher CHEK1 RNA expression in tumor versus normal tissue (log2 FC = +1.532, t-test p < 0.001).
This table shows molecular features associated with CHEK1 in patient tissues and cancer cell lines. In patient samples, CHEK1 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, CHEK1 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 CNS and BLOOD_Leukemia.
