Q-omics provides the consensus-scored CHEK2P2 profile across patient tissues and cancer cell-line models. CHEK2P2 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, CHEK2P2 is differentially expressed in 6, with the highest sampling consensus in HNSC. Additionally, CHEK2P2 RNA expression shows 10,000 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight LIHC, HNSC, and TGCT as cancer lineages where CHEK2P2 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 CHEK2P2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CHEK2P2 survival associations across molecular data types. CHEK2P2 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CHEK2P2 RNA expression–survival associations across cancer types. High CHEK2P2 expression shows unfavorable associations in LIHC, COAD, BRCA, UCEC and PCPG, but favorable associations in KIRC. The LIHC 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 LIHC as the clearest survival context for CHEK2P2 RNA expression.
This table summarizes CHEK2P2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 6. The strongest signals are observed in HNSC for RNA.
This table ranks reproducible tumor–normal expression differences for CHEK2P2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CHEK2P2 shows lower tumor expression in KICH and higher tumor expression in HNSC, UCEC, KIRC, LUSC and LIHC. The HNSC box plot shows higher CHEK2P2 RNA expression in tumor versus normal tissue (log2 FC = +0.054, t-test p = .005).
This table shows molecular features associated with CHEK2P2 in patient tissues and cancer cell lines. In patient samples, CHEK2P2 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, CHEK2P2 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.
