GO:0000075Ontology (GO BP)GO biological process · ~191 member genes
Q-omics provides the Cell cycle checkpoint signaling (GO:0000075) pathway profile, scoring each patient from the combined activity of its roughly 191 member genes. Pathway activity is associated with patient survival in 30 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 17, with the highest sampling consensus in BLCA. Additionally, pathway RNA activity shows 36,803 significant cross-omics associations, again with the highest sampling consensus in STAD. Together, these results highlight KIRC, BLCA, and STAD as cancer lineages where the pathway shows reproducible signals across outcome, tissue activity, and molecular association 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. Pathway-against-pathway and pathway-against-mutation comparisons are not available for ontology entities.
Survival associations
This table summarizes Cell cycle checkpoint signaling survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (30). The rightmost column indicates the cancer type with the highest sampling consensus for each layer.
This table ranks reproducible pathway activity–survival associations across cancer types. High Cell cycle checkpoint signaling activity shows unfavorable associations in KIRC, ACC, MESO, KICH, THCA and KIRP. In the KIRC Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p < 0.001). KIRC ranks highest by sampling consensus for Cell cycle checkpoint signaling.
This table summarizes Cell cycle checkpoint signaling tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 17 cancer types. The strongest signals are in BLCA for RNA.
This table ranks reproducible tumor–normal activity differences for the pathway. A positive fold-change indicates higher activity in tumor tissue. The pathway shows consistently higher tumor activity across BLCA, HNSC, COAD, LUAD, LIHC and LUSC. In the BLCA box plot, tumor samples show higher pathway activity than matched normal samples (log2 FC = +0.118, t-test p < 0.001).
This table shows molecular features associated with Cell cycle checkpoint signaling pathway activity in patient tissues and cancer cell lines. In patient samples, pathway activity is most strongly linked to RNA and protein features, with the largest associated set in STAD. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in LUNG_SCLC.
