GO:0051307Ontology (GO BP)GO biological process · ~8 member genes
Q-omics provides the Meiotic chromosome separation (GO:0051307) pathway profile, scoring each patient from the combined activity of its roughly 8 member genes. Pathway activity is associated with patient survival in 30 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 16, with the highest sampling consensus in BLCA. Additionally, pathway RNA activity shows 34,485 significant cross-omics associations, again with the highest sampling consensus in STAD. Together, these results highlight ACC, 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 Meiotic chromosome separation 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 Meiotic chromosome separation activity shows unfavorable associations in ACC, MESO, KICH, THCA, LIHC and KIRP. In the ACC Kaplan–Meier curve the high-activity group declines faster, consistent with the unfavorable association (log-rank p < 0.001). ACC ranks highest by sampling consensus for Meiotic chromosome separation.
This table summarizes Meiotic chromosome separation tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 16 cancer types, while mass-spec protein activity shows differences in 4. The strongest signals are in KIRC for RNA and HNSC for protein.
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, KIRC, KIRP, LUAD, UCEC and LUSC. In the BLCA box plot, tumor samples show higher pathway activity than matched normal samples (log2 FC = +0.174, t-test p < 0.001).
This table shows molecular features associated with Meiotic chromosome separation 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 PANCREAS.
