GO:0046874Ontology (GO BP)GO biological process · ~5 member genes
Q-omics provides the Quinolinate metabolic process (GO:0046874) pathway profile, scoring each patient from the combined activity of its roughly 5 member genes. Pathway activity is associated with patient survival in 24 of 34 cancer types, with the highest sampling consensus in KIRP. Among the 18 cancer types available for tumor–normal comparison, the pathway is differentially active in 12, with the highest sampling consensus in BLCA. Additionally, pathway RNA activity shows 29,947 significant cross-omics associations, again with the highest sampling consensus in KIRP. Together, these results highlight KIRP, and BLCA 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 Quinolinate metabolic process survival associations by molecular data type. RNA-level pathway activity shows survival associations in the most cancer types (24). 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 Quinolinate metabolic process activity shows favorable associations in KIRP, READ and KIRC, but unfavorable associations in BRCA, STAD and THCA. In the KIRP Kaplan–Meier curve the low-activity group declines faster, consistent with the favorable association (log-rank p = .001). KIRP ranks highest by sampling consensus for Quinolinate metabolic process.
This table summarizes Quinolinate metabolic process tumor–normal activity differences by data type. RNA-level activity shows significant tumor–normal differences in 12 cancer types, while mass-spec protein activity shows differences in 6. The strongest signals are in BLCA for RNA and CCRCC 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 higher tumor activity across KIRC and lower tumor activity in BLCA, LUAD, LUSC, UCEC and KICH. In the BLCA box plot, normal samples show higher pathway activity than tumor samples (log2 FC = −0.129, t-test p < 0.001).
This table shows molecular features associated with Quinolinate metabolic process 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 KIRP. In cancer cell lines, RNA-expression features and functional dependencies dominate, with the largest set in PANCREAS.
